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Data incompleteness and data scarcity are common problems in agroecological modelling. Moreover, agroecological processes depend on historical data that could be fed into a model in a vast number of ways. This work shows a case study of modelling in agroecology using artificial neural networks. The variable to be modelled is sugar cane yield and for this purpose we used climate, soil, and other environmental variables. Regarding the data precision issue, we trained different neural models using monthly and weekly data in order to compare their performance. Furthermore, we studied the influence of using incomplete observations in the training process in order to include them and thus use a larger quantity of input patterns. Our results show that the gain in observations due to the inclusion of incomplete data is preferable in this application. [ABSTRACT FROM AUTHOR]

This paper reviews one nonlinear and two linear projection architectures, in the context of a comparative study, which are used as either alternative or complementary tools in the identification and analysis of anomalous situations by Intrusion Detection Systems (IDSs). Three neural projection models are empirically compared, using real traffic data sets in an IDS framework. The specific multivariate data analysis techniques that drive these models are able to identify different factors or components by studying higher order statistics - variance and kurtosis - in order to display the most interesting projections or dimensions. Our research describes how a network manager is able to diagnose anomalous behaviour in data traffic through visual projection of network traffic. We also emphasize the importance of the time-dependent variable in the application of these projection methods. [ABSTRACT FROM AUTHOR]

This paper shows a strategy based on simulated annealing for the optimal tuning of a PID controller to deal with time-varying delay. The main goal is to minimize the integral time absolute error (ITAE) performance index and the overshoot for a drilling-force control system. The proposed strategy is compared with other classic tuning rules (the Ziegler-Nichols and Cohen-Coon tuning formulas). Other tuning laws derived from genetic algorithms and the Simplex search algorithm for unconstrained optimization are also included in the comparative study. The results demonstrate that simulated annealing provides an optimal tuning of the PID controller, which means better transient response (less overshoot) and less ITAE than with other methods. [ABSTRACT FROM AUTHOR]

Anthropocentrism of computational systems is totally justified when the task concerns to natural language. Computational linguistics systems use to rely on mathematical and statistical formalisms, which are efficient and useful but far from human procedures and therefore not so skilled. The presented work proposes a computational model of natural language reading, called Cognitive Reading Indexing Model (CRIM), inspired by some aspects of human cognition, trying to become as psychologically plausible as possible. The model relies on a semantic neural network and it produces not vectors but nets of activated concepts as text representations. The experimental evaluation shows that the system is suitable for real applications and also to model human reading, and it provides a framework to validate hypothesis from other Cognitive Science fields. [ABSTRACT FROM AUTHOR]

This paper presents a three degrees of freedom orientation tracker as suitable controlling equipment for an automated wheelchair. Mounted at the back of an operator's head by the help of an easy to wear frontlet, the device permanently outputs the user's head posture which can be used as a joystick-like signal. Within an experimental evaluation we demonstrate the applicability of the proposed control interface even for untrained users. [ABSTRACT FROM AUTHOR]

This paper presents an approach to extracting stock market trading rules from stock market data. Trading rules are based on two multi-layer perceptrons, one generating buy signals and one generating sell signals. Inputs of these perceptrons are fed with values of technical indicators computed on historical stock quotations. Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of trading rules is reasonable and the trading rules are able to generate reasonable trading signals, not only over a training period, used in the training process, but also over a test period, unknown during constructing trading rules. Moreover, trading strategies defined by such trading rules are profitable and often outperform the simple Buy&Hold strategy. [ABSTRACT FROM AUTHOR]

This work presents a short introduction to the main ideas behind the design of specific kernel functions when used by machine learning algorithms, for example support vector machines, in the case that involved patterns are described by non-vectorial information. In particular the interval data case will be analysed as an illustrating example: explicit kernels based on the centre-radius diagram will be formulated for closed bounded intervals in the real line. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a new nature-inspired evolutionary technique simulated with bird flocking and fish schooling. However, the biological model of standard PSO ignores the different decision process of each bird. In nature, if one bird finds some food, generally, it will continue to fly surrounding this spot to find other food, and vice versa. Inspired by this phenomenon, a new swarm intelligent methodology- perceptive particle swarm optimization is designed, in which each particle can apperceive its current status within the whole swarm, and make a dynamic decision by adjusting its next flying direction. Furthermore, a mutation operator is introduced to avoid unsuitable adjustment. Simulation results show the proposed algorithm is effective and efficiency. [ABSTRACT FROM AUTHOR]

The shortage of human organs for transplantation is a serious problem, and is exacerbated by the fact that current organ selection and assignment processes discard a significant number of organs deemed non-viable (not suitable) for transplantation. However, these processes ignore the fact that medical specialists may disagree as to whether an organ is viable or not. Therefore, in this paper we propose a novel organ selection process in which transplant physicians, who may be geographically dispersed, deliberate over the viability of an organ. This argument-based deliberation is formalized in a multi-agent system - CARREL +  - that requires the deliberation to adhere to formal rigorous standards acknowledging the safety critical nature of the domain. We believe that this new selection process has the potential to increase the number of organs that current selection processes make available for transplantation, and thus reduce the increasing gap between the demand for and supply of human organs. [ABSTRACT FROM AUTHOR]

In this paper, we describe two different hand image databases. One has been acquired in laboratory condition with a document scanner, and the other one in operational conditions using a webcam and an infrared filter. The experimental part describes some verification experiments and extracts relevant conclusions about image acquisition and biometric classification. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are being intensely used in health care environments to collect biometric signals from patients. This paper presents an augmented reality visual interface based on artificial markers intended to be used by medical staff, to monitor real time information from different kind of sensors attached to the patients in care centers in a fast and flexible way. The system can be applied for any kind of information source. In this work, it has been tested with temperature and humidity sensors. [ABSTRACT FROM AUTHOR]

A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterisation. This challenging operation is very important since it is directly linked with the produced optical component's quality. In order to automate this repetitive and difficult task, microscopy based inspection system is aimed. After a defects detection phase, a classification phase is mandatory to complete optical devices diagnosis because a number of correctable defects are usually present beside the potential "abiding" ones. In this paper is proposed a processing sequence, which permits to extract pertinent low-dimensional defects features from raw microscopy issued image. The described approach is validated by studying MLP neural network based classification on real industrial data using obtained defects features. [ABSTRACT FROM AUTHOR]

The global trend of increasing longevity of modern societies is causing a growing attention to the elderly citizens. The world elderly population's needs create the conditions for deploying new types of services to sustain independence and preserve quality of life. The main effort is to achieve e-tools capable of supplying different levels of disability and of satisfying the needs of each user. We focused on one of the most common problems: mobility limitations and their correlates, with particular attention to cognition. This paper presents a real case study on the impact of mobility assistance technology in patients presenting physical and/or cognitive disabilities. [ABSTRACT FROM AUTHOR]

This paper presents an autonomous intelligent agent with a human thinking reasoning model, based on past experiences. The agent is developed to assist medical staff in geriatric residences. The health care process is a vital function, requiring nature-inspired solutions imitating the residence staff behaviours. An autonomous deliberative Case-Based Planner agent, AGALZ (Autonomous aGent for monitoring ALZheimer patients), is developed and integrated into an environment-aware multi-agent system, named ALZ-MAS (ALZheimer Multi-Agent System), to optimize health care in geriatric residences. ALZ-MAS is capable of obtaining information about the environment through RFID technology. [ABSTRACT FROM AUTHOR]

Multiclass problems with binary SVM classifiers are commonly treated as a decomposition in several binary sub-problems. An open question is how to properly tune all these sub-problems (SVM hyperparameters) in order to have the lowest error rate for a SVM multiclass scheme based on decomposition. In this paper, we propose a new approach to optimize the generalization capacity of such SVM multiclass schemes. This approach consists in a global selection of hyperparameters for sub-problems all together and it is denoted as multi-model selection. A multi-model selection can outperform the classical individual model selection used until now in the literature. An evolutionary algorithm (EA) is proposed to perform multi-model selection. Experimentations with our EA method show the benefits of our approach over the classical one. [ABSTRACT FROM AUTHOR]

In this paper we use Kohonen's Self-Organizing Map (SOM) for surveying the financial status of Spanish companies. From it, we infer which are the most relevant variables, so that a fast diagnostic on their status can be reached and, besides, explained via a few rules of thumb extracted from the behavior of those variables. This map can be used as part of a decision making process, or as a first stage in an automatic classification tool. Results show that variables, identified in an easy and visual way (using SOM and U-Matrix graph), are in agreement with those obtained using parametric and non-parametric tests, which are more complex and difficult to apply. [ABSTRACT FROM AUTHOR]

We present our ideas about the integration of agent technology with other technologies to build specific assistive tools for the people with disabilities and for the new generation of senior citizens. We aim to explore the benefits of these tools to enhance the autonomy of the target user group in their daily life, and in particular in those cases where this autonomy should be shared between the human and the device that is assisting them. [ABSTRACT FROM AUTHOR]

This paper is about the key role of Personalization through Ambient Intelligence in the development of Assistive Technologies for the elders. Ambient Intelligence implies three relatively new technologies: Ubiquitous Computation, Ubiquitous Communication, and Intelligent User Interfaces. We also present our own ideas about the integration of intelligent agent technology with other technologies to build specific assistive tools for the people with disabilities and for the new generation of senior citizens [ABSTRACT FROM AUTHOR]

The analysis of functional data, is a common task in bioinformatics. Spectral data as obtained from mass spectrometric measurements in clinical proteomics are such functional data leading to new challenges for an appropriate analysis. Here we focus on the determination of classification models for such data. In general the available approaches for this task initially transform the spectra into a vector space followed by training a classifier. Hereby the functional nature of the data is typically lost, which may lead to suboptimal classifier models. Taking this into account a wavelet encoding is applied onto the spectral data leading to a compact functional representation. Further the Supervised Neural Gas classifier is extended by a functional metric. This allows the classifier to utilize the functional nature of the data in the modeling process. The presented method is applied to clinical proteom data showing good results. [ABSTRACT FROM AUTHOR]

Unsupervised neural networks are used for constructing virtual reality spaces for visual data mining of gene expression cancer data. Datasets representative of three of the most important types of cancer considered in modern medicine (liver, lung and stomach) are considered in the study. They are composed of samples from normal and tumor tissues, described in terms of tens of thousands of variables, which are the corresponding gene expression intensities measured in microarray experiments. Despite the very high dimensionality of the studied patterns, high quality visual representations in the form of structure-preserving virtual spaces are obtained using SAMANN neural networks, which enables the differentiation of cancerous and noncancerous tissues. The same networks could be used as nonlinear feature generators in a preprocessing step for other data mining procedures. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-objective optimization approach to the problem of computing virtual reality spaces for the visual representation of relational structures (e.g. databases), symbolic knowledge and others, in the context of visual data mining and knowledge discovery. Procedures based on evolutionary computation are discussed. In particular, the NSGA-II algorithm is used as a framework for an instance of this methodology; simultaneously minimizing Sammon's error for dissimilarity measures, and mean cross-validation error on a k-nn pattern classifier. The proposed approach is illustrated with an example from cancer genomics data (e.g. lung cancer) by constructing virtual reality spaces resulting from multi-objective optimization. Selected solutions along the Pareto front approximation are used as nonlinearly transformed features for new spaces that compromise similarity structure preservation (from an unsupervised perspective) and class separability (from a supervised pattern recognition perspective), simultaneously. The possibility of spanning a range of solutions between these two important goals, is a benefit for the knowledge discovery and data understanding process. The quality of the set of discovered solutions is superior to the ones obtained separately, from the point of view of visual data mining. [ABSTRACT FROM AUTHOR]

The incorporation of new biomedical technologies in the diagnosis and prognosis of cancer is changing medicine to an evidence-based diagnosis. We summarize some studies related to brain tumour research in Europe, based on the metabolic information provided by in vivo Magnetic Resonance Spectroscopy (MRS) and transcriptomic profiling observed by DNA microarrays. The first result presents the improvement in brain tumour diagnosis by combining Long TE and Short TE single voxel MR Spectra. Afterwards, a mixture model for binned and truncated data to characterize and classify MRS is reviewed. The classification of Glioblastomas Multiforme and Meningothelial Meningiomas using single-labeling cDNA-based microarrays was studied as proof of principle in the incorporation of genomic information to clinical diagnosis. Finally, we present a Decision Support System for in-vivo classification of brain tumours were the best inferred classifiers are deployed for their clinical use. [ABSTRACT FROM AUTHOR]

Breast cancer relapse prediction is an important step in the complex decision-making process of deciding the type of treatment to be applied to patients after surgery. Some non-linear models, like neural networks, have been successfully applied to this task but they suffer from the problem of extracting the underlying rules, and knowing how the methods operate can help to a better understanding of the cancer relapse problem. A recently introduced constructive algorithm (DASG) that creates compact neural network architectures is applied to a dataset of early breast cancer patients with the aim of testing the predictive ability of the new method. The DASG method works with Boolean input data and for that reason a transformation procedure was applied to the original data. The degradation in the predictive performance due to the transformation of the data is also analyzed using the new method and other standard algorithms. [ABSTRACT FROM AUTHOR]

This work evaluates the efficiency of Independent Component Analysis in conjunction with neural network classifiers to detect microcalcification clusters in digitized mammograms, the most important non invasive sign of breast cancer. The widespread Digital Database for Screening Mammography was used as the source for digitized mammograms. The results seem to suggest that this technique is suitable to deal with the noisy mammogram environment. [ABSTRACT FROM AUTHOR]

This paper describes an integrated clinical support system combining data entry and access with prognostic modelling, for use by the clinician, complemented by a patient information system tailored to the particular characteristics of the individual patient. The core of the system comprises a modelling methodology based on the PLANN-ARD neural network which combines risk staging with automatic rule generation to derive an explanation facility for the risk group allocation of each patient. The aim of the system is to promote better informed decision making on the part of both the clinician and the patient, exploiting the combined potential of analytical methodologies and the internet. [ABSTRACT FROM AUTHOR]

Approximately 50% of all patients with intraocular melanoma die of metastatic disease, despite successful treatment of the primary tumour. The main factors associated with mortality include: tumour diameter; ciliary body involvement; extraocular tumour spread; epithelioid cell type; high mitotic rate; and chromosome 3 loss. We report the development of a web-based prognostic tool, which integrates all these factors. The cohort comprised 2655 patients (1369 male, 1286 female, mean age: 60.86 years) with histopathological data on 1282 patients and cytogenetic information on 405 patients. There were 871 deaths, 517 of which were from metastatic disease. A Conditional Hazard Estimating Neural Network (CHENN) model has been developed, and used to model the survival probability conditioned on observed clinical data. Such model is trained in the Bayesian framework, which allows model training, model regularization, model comparison and feature selection. The CHENN model is nonlinear, embeds the Cox proportional hazards model, and can correct Cox estimates in the cases with a nonlinear relationship between covariates and survival probabilities, and/or the proportional hazards assumption is not verified. As a result of these studies, we can confidently reassure many patients with intraocular melanoma that their survival probability is not significantly worse than that of the general population. This improves their well-being and avoids unnecessary and expensive screening tests. Conversely, we can reliably identify those with a high risk of metastatic death. These patients are referred to an oncologist for systemic screening and several have undergone partial hepatectomy with significant prolongation of life. Our prognostication enhances the feasibility of future randomized, prospective studies evaluating protocols for systemic screening and adjuvant therapy. [ABSTRACT FROM AUTHOR]

DNA copy number amplifications are hallmarks of many cancers. In this work we analyzed data of genome-wide DNA copy number amplifications collected from more than 4500 neoplasm cases. Based on the 0-1 representation of the data, we trained finite mixtures of multivariate Bernoulli distributions using the EM algorithm to describe the inherent structure in the data. The resulting component distributions of the mixtures of Bernoulli distributions yielded plausible and localized amplification patterns. Individual amplification patterns were tested for their role in cancer groups formed with known risk associations. Our detailed analysis of chromosome 1 showed that asbestos-exposure related and hormonal imbalance-associated cancers were clustered and specific chromosome bands, 1p34 and 1q42, were identified. These sites contain cancer genes, which might explain the condition-specific selection of these loci for amplification. [ABSTRACT FROM AUTHOR]

Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based brain-computer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of ANNs as classifiers for a selective attention to auditory stimuli based BCI system. [ABSTRACT FROM AUTHOR]

Evidence-based medicine has grown in stature over the last three decades and is now regarded a key development of modern medicine. The evidence base can be heterogeneous, involving both qualitative knowledge and measured quantitative data. Machine Learning (ML) methods have also begun to establish themselves as an alternative and promising approach to computer-based data analysis in oncology, as this field moves gradually away from being the preserve of traditional statistical analysis. In this paper, we describe the main areas of cancer research in which ML methods are currently being applied, and briefly discuss some of the advantages and disadvantages of their application. [ABSTRACT FROM AUTHOR]

This paper presents a method for estimation of parameters in dynamical systems, applied to a model of the HIV-AIDS epidemics in Cuba. This estimation technique, based upon artificial neural networks, has been successfully applied to robotic systems, whereas the application to epidemiological models is challenged by the possible uncertainty of the model; besides, a state variable exists that is not directly measurable. With regard to the first limitation, a model provided by experts, previously validated by statistical techniques, has been used; with respect to the second drawback, an evaluation of the unknown variable has been carried out from comparisons with other models of the development of the disease. Among the parameters that intervene in the model, three important factors have been considered: the detection rate of the disease, through the contact tracing program; the detection rate through other methods; and the rate of transition to AIDS of previously undetected infected individuals. Results are plausible, according to experts, and they support both the estimation method and the model. [ABSTRACT FROM AUTHOR]

In this paper we describe how the self-organizing map can be applied to the visualization of the evolution and change of Human Immunodeficiency Virus (HIV) quasiespecies, and how this could be converted into a predictive tool. A SOM is trained with a set of nucleic acid sequences from the HIV-1 virus, and the U-Matrix method is applied to discover what natural groups are formed within it. Results show the validity of the method, and allow to discover two groupings within these sets, and what is the evolutionary path taken in them. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new tool of multiple instance learning which is designed using a grammar based genetic programming (GGP) algorithm. We study its application in Web Mining framework to identify web pages interesting for the users. This new tool called GGP-MI algorithm is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) to multiple instance learning. Computational experiments show that, the GGP-MI algorithm obtains competitive results, solves problems of other algorithms, such as sparsity and scalability and adds comprehensibility and clarity in the knowledge discovery process. [ABSTRACT FROM AUTHOR]

Assets Prognostics and Health Management (PHM) is a promising application area for Soft Computing (SC). To better understand PHM requirements, we introduce a decision-making framework in which we analyze PHM decisional tasks. This framework is the cross product of the decision's time horizon and the domain knowledge used by SC models. Within such a framework, we analyze the progression from simple to annotated lexicon, morphology, syntax, semantics, and pragmatics. We use this metaphor to monitor the leverage of domain knowledge in SC to perform anomaly detection, anomaly identification, failure mode analysis (diagnostics), estimation of remaining useful life (prognostics), on-board control, and off board logistics actions. We illustrate a case study in anomaly detection, which is solved by the construction and fusion of an ensemble of diverse detectors, each of which is based on different SC technologies. [ABSTRACT FROM AUTHOR]

In this paper the utility of using the Self Organizing Maps (SOM), in conjunction with U-matrix, to visualize the evolution of a social network community formed by a set of blogs is shown. Weblogs are dynamic websites updated via easy-to-use content management systems whose links tend to mirror or in some cases establish new types of social relations, thereby creating a social network. Analyzing the evolution of this network allows the discovery of emerging social structures and their trends in growth. Here we apply this method to Blogalia, a blog hosting site from which we have a complete set of data. The proposed procedure not only gives some insight on how communities form and evolve, but would also enable to predict the future paths that their members will take. [ABSTRACT FROM AUTHOR]

Spam, also known as Unsolicited Commercial Email (UCE) is becoming a nightmare for Internet users and providers. Machine learning techniques such as the Support Vector Machines (SVM) have achieved a high accuracy filtering the spam messages. However, a certain amount of legitimate emails are often classified as spam (false positive errors) although this kind of errors are prohibitively expensive. In this paper we address the problem of reducing particularly the false positive errors in anti-spam email filters based on the SVM. To this aim, an ensemble of SVMs that combines multiple dissimilarities is proposed. The experimental results suggest that the new method outperforms classifiers based solely on a single dissimilarity and a widely used combination strategy such as bagging. [ABSTRACT FROM AUTHOR]

In this paper we explore the interest of computational intelligence tools in the management of heterogeneous communication networks, specifically to predict congestion, failures and other anomalies in the network that may eventually lead to degradation of the quality of offered services. We show two different applications based on neural and neurofuzzy systems for Quality of Service (QoS) management in next generation networks for V2oIP services. The two examples explained in this paper attempt to predict the communication network resources for new incoming calls, and visualizing by means of self-organizing maps the QoS of a communication network. [ABSTRACT FROM AUTHOR]

In this paper, intrusion detection method using Bayesian Networks was estimated probability values of behavior contexts based on Bayes theory and Indirect relation. The contexts of network-based FTP service was represented Bayesian Networks of graphic types. We profiled concisely network-based FTP behaviors using behavior context by prior, posterior and Indirect relation. And this method be able to visualize behavior profile to detect/analyze anomaly behavior by BF-XML. We achieve simulation to translate audit data of network into BF-XML which is behavior profile of semi-structured data type for anomaly detection and to visualize BF-XML as SVG. [ABSTRACT FROM AUTHOR]

This work introduces an artificial neural network based speed control system for AC induction motors. In motor control, Radial Basis Function (RBF) network and Volt-Hertz (V/f) method have been adopted as neural controller and scalar driving tool respectively. V/Hz method has been preferred for the sake of simplicity and well-known reliability. The proposed control scheme consists of two main parts; a RBF network and a reference model unit. RBF based main controller supplies appropriate control signals to the driving unit to compensate nonlinearity of the induction motor and track the reference model output. Reference model, is very stable linear filter which is supplying set values to be imitated by induction motor. The success of the proposed control scheme has been demonstrated by experimental results; induction motor has been able to track the prescribed speed trajectory with rather small errors and good stability under properly loading conditions. [ABSTRACT FROM AUTHOR]

Real-life applications of intelligent systems that use neural networks require a high degree of success, usability and reliability. Power systems applications can benefit from such intelligent systems; particularly for voltage stabilization. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. This paper presents an intelligent system which detects voltage instability and classifies voltage output of an assumed power distribution system (PDS) as: stable, unstable or overload. The novelty of our work is the use of voltage output images as the input patterns to the neural network for training and generalizing purposes, thus providing a faster instability detection system that simulates a trained operator controlling and monitoring the 3-phase voltage output of the assumed PDS. Experimental results suggest that our method performs well and provides a fast and efficient system for voltage instability detection. [ABSTRACT FROM AUTHOR]

In this work, Self Organizing Map (SOM) is used in order to detect and classify the broken rotor bars and misalignment type mechanical faults that often occur in induction motors which are widely used in industry. The feature vector samples are extracted from the sampled line current of motors with fault and healthy one. These samples are the poles of the AR model which is obtained from the spectrum of sampled line current. The waveforms are obtained from four different 3 hp test motors. Two of them have different number of broken rotor bars, one test motor has misalignment problem and the last one is the healthy motor. Broken rotor bar and misalignment faults are successfully classified and distinguished from the healthy motor using SOM classification with the feature vectors. It is also worth to mention that discrimination of different number of broken rotor bars has been achieved. [ABSTRACT FROM AUTHOR]

The topology verification is an important problem in power system engineering. The paper presents the solution for this problem with use of the method, that is independent of state estimation. The method combines utilization of knowledge about power system and radial basis function networks. It allows to perform the power system topology verification as a series of verification processes for particular nodes of a power network. In the paper the possibility of such decomposition is proved. A principle of the method is described. Next, the computational example of topology verification with use of the characterized method is presented. At the end, the features of the method are analyzed, paying special attention to its efficiency. [ABSTRACT FROM AUTHOR]

Morphologically independent vectors correspond to approximations to the vertices of the convex hull covering the data vectors in high dimensional space. We use Morphological Associative Memories (MAM) for the induction of sets of morphologically independent vectors from data. Simultaneous Localization and Mapping (SLAM) is the process of simultaneously building a map of the environment and localizing the mapping agent. In this paper we explore the realization of non-metric SLAM using a visual information based approach relying on morphologically independent images induced from a mobile robot camera image stream. The selected images are proposed as the landmarks for localization, building simultaneously a qualitative map of the environment. We report results of some experiments on data gathered from an indoor ambient. [ABSTRACT FROM AUTHOR]

In this paper we propose a generic approach to acquire navigation skills for nonholonomic vehicles in unknown environments. The algorithm uses reinforcement learning to update both the vehicle model and the optimal behaviour at the same time. After the training phase, the vehicle is able to explore the environment through a wall-following behaviour. The vehicle can also reach any goal position by the virtual wall concept. The method does not require function interpolation to obtain a good approximation to the optimal behaviour. The learning time was only a few minutes to acquire the wall-following behaviour. Both simulation and experimental results are reported to show the satisfactory performance of the method. [ABSTRACT FROM AUTHOR]

This paper presents a bio-inspired control model for humanoid robots manipulating objects. Humanoids face several genuine problems: 1) they are not fixed (to the ground) therefore extreme forces generate noisy vibrations on the whole platform (robot body) and 2) rigid control (to avoid dynamic modelling) requires high power to be accurate and dramatically limits their autonomy. We compare a velocity vs a position driven control scheme in the framework of object manipulation. The velocity driven control scheme helps smoother control (reducing the jerks). Furthermore, we use an artificial neural network (RBF) to extract some features of the dynamic model automatically complementing the control scheme. Its performance is evaluated using a real robot platform. Experiments were done using the robot's arm and trajectory data was collected during different trials manipulating different objects in order to acquire the model and evaluate how to use it to improve control accuracy. [ABSTRACT FROM AUTHOR]

A neural architecture that makes possible the integration of a kinematic adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller is proposed for nonholonomic mobile robots. The kinematic adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates. The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. The obstacle avoidance adaptive neuro-controller is a neural network that learns to control avoidance behaviors in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an cluttered environment with obstacles. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot's sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to shared control. It consists of combining orders from a mobile and a human. The weight of these orders is obtained by evaluating their corresponding efficiencies but also of his/her condition, which is estimated by continuously monitoring attached biosensors. We rely on a hierarchical architecture where the reactive layer provides a simple and adaptive combination of these sources. We have evaluated the resulting emergent behaviour for different tasks to measure their efficiencies under different circumstances. The system has been successfully tested in real environments from a quantitative and a qualitative point of view. [ABSTRACT FROM AUTHOR]

A Policy Gradient Reinforcement Learning (RL) technique is used to design the low level controllers that drives the joints of articulated mobile robots: A search in the controller's parameters space. There is an unknown value function that measures the quality of the controller respect to the parameters of it. The search is orientated by the approximation of the gradient of the value function. The approximation is made by means of the robot experiences and then the behaviors emerge. This technique is employed in a structure that processes sensor information to achieve coordination. The structure is based on a modularization principle in which complex overall behavior is the result of the interaction of individual ‘simple' components. The simple components used are standard low level controllers (PID) which output is combined, sharing information between articulations and therefore taking integrated control actions. Modularization and Learning are cognitive features, here we endow the robots with this features. Learning experiences in simulated robots are presented as demonstration. [ABSTRACT FROM AUTHOR]

In previous woks we have introduced a distributed neural architecture for the generation of complex behaviors in evolutionary robotics. In this paper we show how this architecture is able to create its own categories about the sensed world of a robot by direct interaction of the body with the environment. The distributed elements of the architecture cooperate to express the categories on an inner world that is easily accessible from the outside. We conclude the paper with an explanation of how the inner world created by the robot can be used to gain some insight into the mind-body problem. [ABSTRACT FROM AUTHOR]

In this paper we suggest a biologically inspired approach to flexible behavior through emotion modeling. We consider emotion to emerge from relational interaction of body, nervous system and world, through sensory-motor attunement of internal parameters to concern-relevant relationships. We interpret such relationships with the notions of collective variable and control parameters. We introduce a simple robotic implementation of this model of appraisal, following the techniques of evolutionary neuro-robotics. [ABSTRACT FROM AUTHOR]

Kernel methods are a class of algorithms whose importance has grown from the 90s in the machine learning field. Their most notable example are Support Vector Machines (SVMs), which are the state of the art for classification problems. Nevertheless, they are applicable to functional approximation problems and there are however several of them available: SVM for regression, Gaussian Process Regression and Least Squares SVM (LS-SVM) for instance. This paper applies and studies these algorithms to a number of Time Series Prediction problems and compares them with some more conventional techniques. [ABSTRACT FROM AUTHOR]