Search

Quantum phenomena are notoriously difficult to grasp. The present paper first reviews the most important quantum concepts in a non-technical manner: superposition, uncertainty, collapse of the wave function, entanglement and non-locality. It then tries to clarify these concepts by examining their analogues in complex, self-organizing systems. These include bifurcations, attractors, emergent constraints, order parameters and non-local correlations. They are illustrated with concrete examples that include Rayleigh-Bénard convection, social self-organization and Gestalt perception of ambiguous figures. In both cases, quantum and self-organizing, the core process appears to be a symmetry breaking that irreversibly and unpredictably 'collapses' an ambiguous state into one of a number of initially equivalent 'eigenstates' or 'attractors'. Some speculations are proposed about the non-linear amplification of quantum fluctuations of the vacuum being ultimately responsible for such symmetry breaking., Author(s): Francis Heylighen [sup.1] Author Affiliations: (1) grid.8767.e, 0000 0001 2290 8069, Center Leo Apostel, Vrije Universiteit Brussel, , Brussels, Belgium Introduction Quantum mechanics is a theory notorious for the [...]

Online open collaboration efforts, such as Wikipedia articles and open source software development, often involve a large crowd with diverse experiences and interests. Diversity, on the one hand, facilitates the access to and integration of a wide variety of information; on the other hand, it may cause conflict and hurt group performance. Although diversity's effects have been the subject of many studies in offline work groups (with the results remaining inconclusive), its effects in online self-organizing groups are underexplored. In this paper, we examine 648 WikiProjects to understand (1) how tenure disparity and interest variety affect group productivity and member withdrawal and (2) how the two types of diversity evolve over time. Our results show a curvilinear effect of tenure disparity, which increases productivity and decreases member withdrawal, up to a point. Beyond that point, productivity slightly decreases, and members are more likely to withdraw. In comparison, our results show a positive effect of interest variety on productivity and no significant effects on withdrawal. We also find that, over a project's life cycle, tenure disparity decreases and interest variety increases, with both converging toward the level that is optimal for group performance. Overall, our study highlights the importance of having diverse experiences and perspectives in online open collaboration and the power of self-organizing that helps groups evolve toward their high-performing zones. It also has practical implications on the design of collaboration tools and new forms of organizing work in traditional organizations. Keywords: online open collaboration; self-organizing groups; diversity; group performance; Wikipedia History: Received September 22, 2010; accepted October 24, 2014, by Sandra Slaughter, information systems. Published online in Articles in Advance August 28, 2015., 1. Introduction Computer technologies have enabled new forms of open collaboration in self-organizing groups on the Internet, as with Wikipedia and open source software projects. More recently, people have initiated [...]

A field trial was conducted to show that automatic neighbor relation (ANR) works successfully in a real-life environment. ANR is utilized to automatically generate information in neighbor relation tables for handovers. Solutions for problematic handover scenarios and self-optimization of relevant handover parameters are also discussed.

The drivers for Long Term Evolution (LTE) self-optimizing networks (SON), Alcatel-Lucent's vision for SON, are discussed. It is believed that LTE has the ability to bring in high data rates with the high quality of service (QoS) required for various applications without extensive operational overhead.

In this paper the concept of program management in complex governance processes is considered from a self-organization perspective. First the emergence of program management is discussed. Subsequently it is argued that the major challenge for program management in complex processes is about realizing connective capacity. The paper concludes with the creation of a self-organization theory framework to analyze connective capacity in complex governance processes, built on insights from conservative and dissipative self-organization in complex systems., Introduction In this paper the concept of program management in complex governance processes is considered from a self-organization perspective. First the emergence of program management is discussed. Subsequently it is [...]

One of the most important properties of systems is complexity. In a simple way, we can define the complexity of a system in terms of the number of elements that it contains, the nature and number of interrelations and the number of levels of embeddedness. When a high level of complexity exists in a system, it is considered a complex system. Complex systems can be soft systems and hard systems. In hard systems, when their elements are interrelated in a non-linear way, they are considered complex systems when they contain a great number of elements interacting in a non-linear way. To try to understand the behaviour of this type of system diverse mathematical tools have been developed. A new scientific discipline with great impact in the analysis of the complex systems has been developed in recent years, called fractal analysis. The study of the complex systems in the framework of fractal theory has been recognized as a new scientific discipline, being sustained by advances that have been made in diverse fields ranging from physics to economics. In this paper the history and the basic concepts of the fractal analysis of complex systems are discussed briefly and an oil crude price volatility fractal analysis is provided. Keywords complexity; complex systems; self-organization fractals; oil crude market, INTRODUCTION One of the most exciting and rapidly developing areas of modern research is the quantitative study of complexity (Balankin, 1991; Chorin and Wright, 1999). Complexity has had a specially [...]

The reusability in learning objects has always been a hot issue. However, we believe that current approaches to e- Learning failed to find a satisfying answer to this concern. This paper presents an approach that enables capitalization of existing learning resources by first creating 'content metadata' through text mining and natural language processing and second by creating dynamically learning knowledge objects, i.e., active, adaptable, reusable, and independent learning objects. The proposed model also suggests integrating explicitly instructional theories in an on-the-fly composition process of learning objects. Semantic Web technologies are used to satisfy such an objective by creating an ontology-based organizational memory able to act as a knowledge base for multiple training environments. Index Terms--Applications and expert knowledge-intensive systems, computer-managed instruction, intelligent Web services and semantic Web, knowledge management.

The rapid evolution of information and communication technology opens a wide spectrum of opportunities to change our surroundings into an Ambient Intelligent (Aml) world. Aml is a vision of future information society, where people are surrounded by a digital environment that is sensitive to their needs, personalized to their requirements, anticipatory of their behavior, and responsive to their presence. It emphasizes on greater user friendliness, user empowerment, and more effective service support, with an aim to bring information and communication technology to everyone, every home, every business, and every school, thus improving the quality of human life. Aml unprecedentedly enhances learning experiences by endowing the users with the opportunities of learning in context, a breakthrough from the traditional education settings. In this survey paper, we examine some major characteristics of an Aml learning environment. To deliver a feasible and effective solution to ambient learning, we overview a few latest developed enabling technologies in context awareness and interactive learning. Associated practices are meanwhile reported. We also describe our experience in designing and implementing a smart class prototype, which allows teachers to simultaneously instruct both local and remote students in a context-aware and natural way. Index Terms--Learning, ambient intelligence, context aware, interactive learning, RFID, smart classroom.

The Sharable Content Object Reference Model (SCORM) specification defines metadata of learning objects, which are used as the elementary reusable components in distance learning. The Content Object Repository Discovery and Registration/ Resolution Architecture (CORDRA) specification provides a common architecture for the resolution, discovery, and sharing of these learning objects. These two specifications together define standardized ways in which learning objects can be discovered and reused by content designers. However, the current CORDRA and the definition of objects in SCORM only allow an object to be copied, updated, and reorganized in a new content aggregation, which is used as a delivery package to end users. This paper proposes a revised CORDRA architecture and a reusability mechanism to make instruction design easier. In particular, it proposes a structure called a reusability tree for tracking the history of reuse of learning objects in CORDRA. This paper also defines the notions of similarity, diversity, and relevancy of learning objects to make it easier for users to precisely search for and reuse learning objects. Index Terms--SCORM, CORDRA, reusability, learning object metadata, distributed system, distance learning.

Real-time interactive virtual classroom with teleeducation experience is an important approach in distance learning. However, most current systems fail to meet new challenges in extensibility and scalability, which mainly lie with three issues. First, an open system architecture is required to better support the integration of increasing human-computer interfaces and personal mobile devices in the classroom. Second, the learning system should facilitate opening its interfaces, which will help easy deployment that copes with different circumstances and allows other learning systems to talk to each other. Third, problems emerge on binding existing systems of classrooms together in different places or even different countries such as tackling systems intercommunication and distant intercultural learning in different languages. To address these issues, we build a prototype application called Open Smart Classroom built on our software infrastructure based on the multiagent system architecture using Web Service technology in Smart Space. Besides the evaluation of the extensibility and scalability of the system, an experiment connecting two Open Smart Classrooms deployed in different countries is also undertaken, which demonstrates the influence of these new features on the educational effect. Interesting and optimistic results obtained show a significant research prospect for developing future distant learning systems. Index Terms--Pervasive computing, computer uses in education, Web-based services.

A global education system, as a key area in future IT, has fostered developers to provide various learning systems with low cost. While a variety of e-learning advantages has been recognized for a long time and many advances in e-learning systems have been implemented, the needs for effective information sharing in a secure manner have to date been largely ignored, especially for virtual university collaborative environments. Information sharing of virtual universities usually occurs in broad, highly dynamic network-based environments, and formally accessing the resources in a secure manner poses a difficult and vital challenge. This paper aims to build a new rule-based framework to identify and address issues of sharing in virtual university environments through role-based access control (RBAC) management. The framework includes a role-based group delegation granting model, group delegation revocation model, authorization granting, and authorization revocation. We analyze various revocations and the impact of revocations on role hierarchies. The implementation with XML-based tools demonstrates the feasibility of the framework and authorization methods. Finally, the current proposal is compared with other related work. Index Terms--E-learning, RBAC, role-based delegation, revocation.

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested. Index Terms--Community discovery, e-learning, emerging semantics, semantic community, Semantic Link Network.

The sensitivity of the constrained K-means clustering algorithm (Cop-Kmeans) to the assignment order of instances is studied, and a novel assignment order learning method for Cop-Kmeans, termed as clustering Uncertainty-based Assignment order Learning Algorithm (UALA), is proposed in this correspondence paper. The main idea of UALA is to rank all instances in the data set according to their clustering uncertainties calculated by using the ensembles of multiple clustering algorithms. Experimental results on several real data sets with artificial instance-level constraints demonstrate that UALA can identify a good assignment order of instances for Cop-Kmeans. In addition, the effects of ensemble sizes on the performance of UALA are analyzed, and the generalization property of Cop-Kmeans is also studied. Index Terms--Constrained K-means clustering algorithm (Cop-Kmeans), ensemble learning, instance-level constraints.

Recently, graph-based semisupervised learning methods have been widely applied in multimedia research area. However, for the application of video semantic annotation in multilabel setting, these methods neglect an important characteristic of video data: The semantic concepts appear correlatively and interact naturally with each other rather than exist in isolation. In this paper, we adapt this semantic correlation into graph-based semisupervised learning and propose a novel method named correlative linear neighborhood propagation to improve annotation performance. Experiments conducted on the Text REtrieval Conference VIDeo retrieval evaluation data set have demonstrated its effectiveness and efficiency. Index Terms--Graph-based method, label propagation, semantic correlation, video annotation.

The self-organizing network (SON)-based multiple model system is a recently proposed method for identifying the dynamics of a general nonlinear system. It has been observed by researchers that cooperative learning among neighboring regions is sometimes important for the success of identification of a nonlinear system under the multiple model system framework. In this paper, we intend to formally evaluate the effects of cooperative learning and topology preservation in identification of multiple model system based on SON. The results of the mathematical analysis supports the heuristic that a good learning strategy for identifying the local model parameters of a SON-based multiple model system is to choose a neighborhood function whose effective region is initially wider and is reduced gradually during learning. An example of nonlinear function approximation is also provided at the end of this paper to demonstrate the results of the mathematical analysis. Index Terms--Cooperative learning, multiple model system, self-organizing network (SON), topology preservation.

The article proposes a modified self-organizing neural network for the estimation of the disparity map from a stereo pair of images. The network performs better than many existing algorithms on synthetic and natural stereo pairs.

As a typical data visualization technique, self-organizing map (SOM) has been extensively applied to data clustering, image analysis, dimension reduction, and so forth. In a conventional adaptive SOM, it needs to choose an appropriate learning rate whose value is monotonically reduced over time to ensure the convergence of the map, meanwhile being kept large enough so that the map is able to gradually learn the data topology. Otherwise, the SOM's performance may seriously deteriorate. In general, it is nontrivial to choose an appropriate monotonically decreasing function for such a learning rate. In this letter, we therefore propose a novel rival-model penalized self-organizing map (RPSOM) learning algorithm that, for each input, adaptively chooses several rivals of the best-matching unit (BMU) and penalizes their associated models, i.e., those parametric real vectors with the same dimension as the input vectors, a little far away from the input. Compared to the existing methods, this RPSOM utilizes a constant learning rate to circumvent the awkward selection of a monotonically decreased function for the learning rate, but still reaches a robust result. The numerical experiments have shown the efficacy of our algorithm. Index Terms--Constant learning rate, rival-model penalized self-organizing map (RPSOM), self-organizing map (SOM).

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns. Index Terms--Data mining, demand management, electrical customer segmentation, load patterns, self-organizing maps (SOMs).

In this letter, a new self-organizing map (SOM) algorithm with computational cost O([log.sub.2] M) is proposed where [M.sup.2] is the size of a feature map. The first SOM algorithm with O([M.sup.2]) was originally proposed by Kohonen. The proposed algorithm is composed of the subdividing method and the binary search method. The proposed algorithm does not need the neighborhood functions so that it eliminates the computational cost in learning of neighborhood vectors and the labor of adjusting the parameters of neighborhood functions. The effectiveness of the proposed algorithm was examined by an analysis of codon frequencies of Escherichia colt (E. colt) K12 genes. These drastic computational reduction and accessible application that requires no adjusting of the neighborhood function will be able to contribute to many scientific areas. Index Terms--Binary search, computational reduction, codon frequency, Escherichia coli (E. coli), neighborhood function, self-organizing map (SOM), subdividing method.

In this paper, we introduce feedback-based associative learning in self-organized learning arrays (SOLAR). SOLAR structures are hierarchically organized networks of sparsely connected neurons that define their own functions and select their interconnections locally. This paper provides a description of neuron self-organization and signal processing. Feedforward processing is used to make necessary correlations and learn the input patterns. Discovered associations between neuron inputs are used to generate feedback signals. These feedback signals, when propagated to the primary inputs, can establish the expected input values. This can be used for heteroassociative (HA) and autoassociative (AA) learning and pattern recognition. Example applications in HA learning are given. Index Terms--Associative learning, feedback structure, pattern recognition, self-organizing learning array (SOLAR).

In this paper, we propose a new approach to function approximation based on a growing neural gas (GNG), a self-organizing map (SOM) which is able to adapt to the local dimension of a possible high-dimensional input distribution. Local models are built interpolating between values associated with the map's neurons. These models are combined using a weighted sum to yield the final approximation value. The values, the positions, and the 'local ranges' of the neurons are adapted to improve the approximation quality. The method is able to adapt to changing target functions and to follow nonstationary input distributions. The new approach is compared to the radial basis function (RBF) extension of the growing neural gas and to locally weighted projection regression (LWPR), a state-of-the-art algorithm for incremental non-linear function approximation. Index Terms--Function approximation, growing neural gas (GNG), locally weighted learning, radial basis functions (RBFs), self-organizing maps (SOMs).

We present a statistical analysis of a large set of absorption spectra of phytoplankton, measured in natural samples collected from ocean water, in conjunction with detailed pigment concentrations. We processed the absorption spectra with a sophisticated neural network method suitable for classifying complex phenomena, the so-called self-organizing maps (SOM) proposed by Kohonen [Kohonen, Self Organizing Maps (Springer-Verlag, 1984)]. The aim was to compress the information embedded in the data set into a reduced number of classes characterizing the data set, which facilitates the analysis. By processing the absorption spectra, we were able to retrieve well-known relationships among pigment concentrations and to display them on maps to facilitate their interpretation. We then showed that the SOM enabled us to extract pertinent information about pigment concentrations normalized to chlorophyll a. We were able to propose new relationships between the fucoxanthin/Tchl-a ratio and the derivative of the absorption spectrum at 510 nm and between the Tchl-b/Tchl-a ratio and the derivative at 640 nm. Finally, we demonstrate the possibility of inverting the absorption spectrum to retrieve the pigment concentrations with better accuracy than a regression analysis using the Tchl-a concentration derived from the absorption at 440 nm. We also discuss the data coding used to build the self-organizing map. This methodology is very general and can be used to analyze a large class of complex data.

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay. Index Terms--EMG, linear-nonlinear feature projection, pattern recognition, principal components analysis, self-organizing feature map, wavelet packet transform.

Abstract--The problem of manipulator control is a highly complex problem of controlling a system which is multi-input, multi-output, non-linear and time variant. In this paper some adaptive fuzzy, and a [...]

This paper presents a recurrence plot scheme approach to the analysis of nonstationary transition patterns of IP-network traffic. In performing a quantitative assessment of dynamical transition patterns of IP-network traffic, we used the values of determinism (DET) defined by the recurrence quantification analysis (RQA). Also, in evaluating fractal-related properties of IP-network traffic, we employed the detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range dependence (LRD) in nonstationary time-series signals. Furthermore, to obtain a comprehensive view of network traffic conditions, we used a self-organizing map, which provides a way to map high-dimensional data onto a low-dimensional domain. When applying this method to traffic analysis, we performed two kinds of traffic measurement in Tokyo, Japan, and derived values of DET and the LRD-based scaling parameter [aplha] of IP-network traffic. Then, we found that the characteristic with respect to DET and self-similarity seen in the measured traffic fluctuated over time, with different time variation patterns for two measurements. In training the self-organizing map, we used three parameters: average throughput, variation ratio of DET, and value. As a result, we visually confirmed that the traffic data could be projected onto the map in accordance with traffic properties, resulting in a combined depiction of the effects of the DET and network utilization rates on the time-variations of LRD. Index Terms--Detrended fluctuation analysis (DFA), network traffic, nonstationary transition, recurrence plot (RP).

A new lattice disentangling monitoring algorithm for a hybrid self-organizing map-kernel-based maximum entropy learning rule (SOM-kMER) model is proposed. It aims to overcome topological defects owing to a rapid decrease of the neighborhood range over the finite running time in topographic map formation. The empirical results demonstrate that the proposed approach is able to accelerate the formation of a topographic map and, at the same time, to simplify the monitoring procedure. Index Terms--Kernel-based maximum entropy learning rule (kMER), kernel-based topographic map, lattice disentangling, self-organizing map (SOM).

Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new online method for learning maps of unknown dynamic worlds. For this purpose the new Prune-able fuzzy adaptive resonance theory neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network with novel mechanisms that provide important dynamic adaptation capabilities. Relevant PAFARTNA properties are formulated and demonstrated. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. The proposed methods are integrated into a navigation architecture. With the new navigation architecture the mobile robot is able to navigate in changing worlds, and a degree of optimality is maintained, associated to a shortest path planning approach implemented in real-time over the underlying global world model. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods. Index Terms--Dynamic worlds, mobile robot navigation, neural architecture, self-organizing maps learning.

In this paper, the authors propose and investigate a new category of neurofuzzy networks--fuzzy polynomial neural networks (FPNNs)--and develop a comprehensive design methodology involving mechanisms of genetic optimization and, in particular, genetic algorithms (GAs). The conventional FPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended group method of data handling, with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The design proposed in this paper addresses this issue. The augmented genetically optimized FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison with the one encountered in the conventional FPNN. The GA-based design procedure that is applied to each layer of FPNN leads to the selection of the preferred nodes (or fuzzy polynomial neurons) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs, whereas the ensuing, detailed parametric optimization is carried out in the setting of a standard least-square-method-based learning. The performance of gFPNN is quantified through experimentation where a number of modeling benchmarks are being used, i.e., synthetic and experimental data already experimented within fuzzy or neurofuzzy modeling. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models. Index Terms--Fuzzy polynomial neuron (FPN), genetic algorithms (GAs), genetically optimized fuzzy polynomial neural networks (gFPNNs), multilayer perceptron (MLP), self-organizing network.

A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is applied to accretively learn the output clusters of the neural network. One significant advantage of this is its ability to detect individual anomaly types that are hard to detect with other anomaly-detection schemes. Applying this technique, several feature subsets of the tcptrace network-connection records that give above 95% detection at false-positive rates below 5% were identified. Index Terms--Accretive construction, anomaly detection, confidence measurement, hyperellipsoidal clustering, neural networks, self-organizing map (SOM).

In this paper, we study the problem of motif discoveries in unaligned DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complexity and reliability of the search algorithms. We propose a self-organizing neural network structure for solving the problem of motif identification in DNA and protein sequences. Our network contains several layers, with each layer performing classifications at different levels. The top layer divides the input space into a small number of regions and the bottom layer classifies all input patterns into motifs and nonmotif patterns. Depending on the number of input patterns to be classified, several layers between the top layer and the bottom layer are needed to perform intermediate classifications. We maintain a low computational complexity through the use of the layered structure so that each pattern's classification is performed with respect to a small subspace of the whole input space. Our self-organizing neural network will grow as needed (e.g., when more motif patterns are classified). It will give the same amount of attention to each input pattern and will not omit any potential motif patterns. Finally, simulation results show that our algorithm outperforms existing algorithms in certain aspects. In particular, simulation results show that our algorithm can identify motifs with more mutations than existing algorithms. Our algorithm works well for long DNA sequences as well. Index Terms--DNA sequences, motif finding, neural networks, protein sequences, self-organization, subtle signals.

In this letter, a preliminary study of hahituation in self-organizing networks is reported. The habituation model implemented allows us to obtain a faster learning process and better clustering performances. The habituable neuron is a generalization of the typical neuron and can be used in many self-organizing network models. The habituation mechanism is implemented in a SOM and the clustering performances of the network are compared to the conscience learning mechanism that follows roughly the same principle but is less sophisticated. Index Terms--Conscience learning, habituation, self-organizing feature maps, unsupervised learning.

In this paper, we enhance and analyze the Evolving Tree (ETree) data analysis algorithm. The suggested improvements aim to make the system perform better while still maintaining the simple nature of the basic algorithm. We also examine the system's behavior with many different kinds of tests, measurements and visualizations. We compare the ETree's performance against classical data analysis methods and very similar modern systems. We find that the ETree is a suitable method for unsupervised analysis of huge data sets. Index Terms--Evolving tree (ETree), hierarchical clustering, self-organization, tree-shaped neural networks.

The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a self-organizing map (SOM)-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV. Index Terms--Inverse controller, local linear model, multiple models, self-organizing map (SOM).

The self-organizing map (SOM) is an unsupervised neural network which projects high-dimensional data onto a low-dimensional grid and visually reveals the topological order of the original data. Self-organizing maps have been successfully applied to many fields, including engineering and business domains. However, the conventional SOM training algorithm handles only numeric data. Categorical data are usually converted to a set of binary data before training of an SOM takes place. If a simple transformation scheme is adopted, the similarity information embedded between categorical values may be lost. Consequently, the trained SOM is unable to reflect the correct topological order. This paper proposes a generalized self-organizing map model that offers an intuitive method of specifying the similarity between categorical values via distance hierarchies and, hence, enables the direct process of categorical values during training. In fact, distance hierarchy unifies the distance computation of both numeric and categorical values. The unification is done by mapping the values to distance hierarchies and then measuring the distance in the hierarchies. Experiments on synthetic and real datasets were conducted, and the results demonstrated the effectiveness of the generalized SOM model. Index Terms--Categorical data, cluster analysis, distance hierarchy, neural networks, self-organizing map (SOM).

The parameterless self-organizing map (PLSOM) is a new neural network algorithm based on the self-organizing map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighborhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions. Index Terms--Self-organizing feature maps.

In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called [alpha]-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or [alpha]-agents, and virtual agents associated with [alpha]-agents called [beta]- and [gamma]-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms. Index Terms--Consensus theory, distributed control, dynamic graphs, mobile sensor networks, networked autonomous vehicles, self-assembly of networks, self-organizing systems, swarms.

A fuzzy-similarity-based self-organized network inspired by immune algorithm (F-SONIA) is proposed in order to develop an artificial odor discrimination system for three-mixture-fragrance recognition. It can deal with an uncertainty in frequency measurements, which is inherent in odor acquisition devices, by employing a fuzzy similarity. Mathematical analysis shows that the use of the fuzzy similarity results on a higher dissimilarity between fragrance classes, therefore, the recognition accuracy is improved and the learning time is reduced. Experiments show that F-SONIA improves recognition accuracy of SONIA by 3%-9% and the previously developed artificial odor discrimination system by 14%-25%. In addition, the learning time of F-SONIA is three times faster than that of SONIA. Index Terms--Artificial odor discrimination, fuzzy similarity, immune algorithm, self-organized network, three-mixture-fragrance problem.

This paper proposed a novel approach for the Power Quality (PQ) disturbances classification based on the wavelet transform and self organizing learning array (SOLAR) system. Wavelet transform is utilized to extract feature vectors for various PQ disturbances based on the multiresolution analysis (MRA). These feature vectors then are applied to a SOLAR system for training and testing. SOLAR has three advantageous over a typical neural network: data driven learning, local interconnections and entropy based self-organization. Several typical PQ disturbances are taken into consideration in this paper. Comparison research between the proposed method, the support vector machine (SVM) method and existing literature reports show that the proposed method can provide accurate classification results. By the hypothesis test of the averages, it is shown that there is no statistically significant difference in performance of the proposed method for PQ classification when different wavelets are chosen. This means one can choose the wavelet with short wavelet filter length to achieve good classification results as well as small computational cost. Gaussian white noise is considered and the Monte Carlo method is used to simulate the performance of the proposed method in different noise conditions. Index Terms--Noise, power quality (PQ), self-organizing learning array (SOLAR), support vector machine (SVM), wavelet transform.

Understanding the complexity and dynamics of cancer cells in response to effective therapy requires hypothesis-driven, quantitative, and high-throughput measurement of genes and proteins at both spatial and temporal levels. This study was designed to gain insights into molecular networks underlying the clinical synergy between retinoic acid (RA) and arsenic trioxide (ATO) in acute promyelocytic leukemia (APL), which results in a high-quality disease-free survival in most patients after consolidation with conventional chemotherapy. We have applied an approach integrating cDNA microarray, 2D gel electrophoresis with MS, and methods of computational biology to study the effects on APL cell line NB4 treated with RA, ATO, and the combination of the two agents and collected in a time series. Numerous features were revealed that indicated the coordinated regulation of molecular networks from various aspects of granulocytic differentiation and apoptosis at the transcriptome and proteome levels. These features include an array of transcription factors and cofactors, activation of calcium signaling, stimulation of the IFN pathway, activation of the proteasome system, degradation of the PML-RAR[alpha] oncoprotein, restoration of the nuclear body, cell-cycle arrest, and gain of apoptotic potential. Hence, this investigation has provided not only a detailed understanding of the combined therapeutic effects of RA/ATO in APL but also a road map to approach hematopoietic malignancies at the systems level. systems biology | self-organizing map

The methods to integrate latent learning with dynamic programming under the Learning Classifier System (LCS) are described. The Modular Anticipatory Classifier System (MACS) is proposed as the latest model by showing some limitations of the formalism common to Anticipatory Classifier System (ACS) and Yet Another Classifier System (YACS).

This note deals with the initial shift problem that arises from discrete-time iterative learning control. A unified learning scheme is considered for a class of nonlinear systems with well-defined relative degree, which adopts the error data with anticipation in time and provides wider freedom for the updating law formation. The sufficient convergence condition is derived to enable the system to possess asymptotic tracking capability and the converged output trajectory can be assessed by the initial condition. The tracking performance is improved further by the introduction of initial rectifying action and the complete tracking is achieved over a specified interval. Index Terms--Discrete-time, initial condition problem, learning control, nonlinear systems, relative degree.

Modern science confronts us with massive amounts of data: expression profiles of thousands of human genes, multimedia documents, subjective judgments on consumer products or political candidates, trade indices, global climate patterns, etc. These data are often highly structured, but that structure is hidden in a complex set of relationships or high-dimensional abstractions. Here we present a self-organizing algorithm for embedding a set of related observations into a low-dimensional space that preserves the intrinsic dimensionality and metric structure of the data. The embedding is carried out by using an iterative pairwise refinement strategy that attempts to preserve local geometry while maintaining a minimum separation between distant objects. In effect, the method views the proximities between remote objects as lower bounds of their true geodesic distances and uses them as a means to impose global structure. Unlike previous approaches, our method can reveal the underlying geometry of the manifold without intensive nearest-neighbor or shortest-path computations and can reproduce the true geodesic distances of the data points in the low-dimensional embedding without requiring that these distances be estimated from the data sample. More importantly, the method is found to scale linearly with the number of points and can be applied to very large data sets that are intractable by conventional embedding procedures.