Search

Considered are controllable systems of (structured) objects s ∈ S, S a non-empty set, to which time instants (time "points") $t \in \it T$ of a partial ordered time (T, <) are assigned. Treated are topological concepts, a theory of controllable variables, ordering relations on pow (T,<) induced by <, discrete and continuous processes (st)$_{t\in{\it U}\subseteq{\it T}}$, relations of processes, a general theory of algorithms, neighborhoods of processes, process approximations, and controllable evolutionary processes. Care has been taken of causality and time dependencies of physical processes. [ABSTRACT FROM AUTHOR]

Selection of an optimal subset of classifiers in designing classifier ensembles is an important problem. The search algorithms used for this purpose maximize an objective function which may be the combined training accuracy or diversity of the selected classifiers. Taking into account the fact that there is no benefit in using multiple copies of the same classifier, it is generally argued that the classifiers should be diverse and several measures of diversity are proposed for this purpose. In this paper, the relative strengths of combined training accuracy and diversity based approaches are investigated for the plurality voting based combination rule. Moreover, we propose a diversity measure where the difference in classification behavior exploited by the plurality voting combination rule is taken into account. [ABSTRACT FROM AUTHOR]

This paper presents a wrapper method for feature selection that combines Lazy Learning, racing and subsampling techniques. Lazy Learning (LL) is a local learning technique that, once a query is received, extracts a prediction by locally interpolating the neighboring examples of the query which are considered relevant according to a distance measure. Local learning techniques are often criticized for their limitations in dealing with problems with high number of features and large samples. Similarly wrapper methods are considered prohibitive for large number of features, due to the high cost of the evaluation step. The paper aims to show that a wrapper feature selection method based on LL can take advantage of two effective strategies: racing and subsampling. While the idea of racing was already proposed by Maron and Moore, this paper goes a step further by (i) proposing a multiple testing technique for less conservative racing (ii) combining racing with sub-sampling techniques. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent agent-inspired genetic algorithm (IAGA). Analogous to the intelligent agent, each individual in IAGA has its own properties, including crossover probability, mutation probability, etc. Numerical simulations demonstrate that, compared with the standard GA where all individuals in a population share the same crossover and mutation probabilities, the proposed algorithm is more flexible, efficient and effective. [ABSTRACT FROM AUTHOR]

The task of estimating the meteorological profile of any location of interest within a specified area is undertaken. Assuming that the meteorological profiles of a sufficient number of representative reference locations within the specified area are available, the proposed methodology is based on (a) the organisation of the meteorological profiles of the reference locations employing a self-organising map (SOM) and (b) the classification of the most salient morphological characteristics of the reference locations. Subsequently, the meteorological profile of any novel location of interest is approximated by a weighted average of the meteorological profiles represented on the SOM for those reference locations whose morphological characteristics most closely match the morphological characteristics of the location of interest. The proposed methodology is evaluated by comparing the accuracy of meteorological profile estimation with that of existing estimation techniques as well as with the actual meteorological profiles of the locations of interest. [ABSTRACT FROM AUTHOR]

A growing self-organizing (SOM) neural network, enhanced with a local search heuristic is proposed as an efficient traveling salesman problem solver. A ring structure of processing units is evolved in time with a Kohonen type adaptation dynamics together with a simple growing rule in the number of processing units. The result is a neural network heuristic for the TSP with a computational complexity of O(n2), comparable to other reported SOM-like networks. The tour emerging from the SOM network is enhanced by the application of a simple greedy 2-Opt local search. Experiments over a broad set of TSP instances are carried out. The experimental results show a solution accuracy equivalent to that of the best SOM based heuristics reported in the literature. [ABSTRACT FROM AUTHOR]

Subpath reversals are common operations in graph-based structures arising in a wide range of applications in combinatorial optimization. We describe the satellite list, a variation on the doubly-linked list that is symmetric, efficient, and can be reversed or reverse subsections in constant time. [ABSTRACT FROM AUTHOR]

Today numerous variants of heuristic optimization algorithms are used to solve different kinds of optimization problems. This huge variety makes it very difficult to reuse already implemented algorithms or problems. In this paper the authors describe a generic, extensible, and paradigm-independent optimization environment that strongly abstracts the process of heuristic optimization. By providing a well organized and strictly separated class structure and by introducing a generic operator concept for the interaction between algorithms and problems, HeuristicLab makes it possible to reuse an algorithm implementation for the attacking of lots of different kinds of problems and vice versa. Consequently HeuristicLab is very well suited for rapid prototyping of new algorithms and is also useful for educational support due to its state-of-the-art user interface, its self-explanatory API and the use of modern programming concepts. [ABSTRACT FROM AUTHOR]

The parallelization of gradient descent training algorithm with momentum and the Levenberg-Marquardt algorithm is implemented using C# and Message Passing Interface (MPI) on .NET platform. The turnaround times of both algorithms are analyzed on cluster of homogeneous computers. It is shown that the optimal number of cluster nodes is a compromise between the decrease of computational time due to parallelization and corresponding increase of time needed for communication. [ABSTRACT FROM AUTHOR]

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable. The paper argues that the benefits that can be obtained with the proposed approach is potentially higher than those obtained with traditional parallel genetic algorithms. [ABSTRACT FROM AUTHOR]

This paper discusses a novel performance driven placement technique based on distributed Genetic Algorithms, and focuses particularly on the following points:(l) The algorithm has two-level hierarchical structure consisting of outline placement and detail placement. (2) For selection control, which is one of the genetic operations, new multi-objective functions are introduced. (3) In order to reduce the computation time, a parallel processing is introduced. Results show improvement of 22.5% for worst path delay, 11.7% for power consumption, 15.9% for wire congestion and 10.7% for chip area. [ABSTRACT FROM AUTHOR]

The paper deals with assessing performance improvements and some implementation issues of two well-known data mining algorithms, Apriori and FP-growth, in Alchemi grid environment. We compare execution times and speed-up of two parallel implementations: pure Apriori and hybrid FP-growth — Apriori version on grid with one to six processors. As expected, the latter shows superior performances. We also discuss the effects of database characteristics on overall performance, and give directions for proper choice of execution parameters and suitable number of executors. [ABSTRACT FROM AUTHOR]

In this paper we introduce technique how a neural network can generate a Hidden Markov Chain. We use neural network called Temporal Information Categorizing and Learning Map. The network is an enhanced version of standard Categorizing and Learning Module (CALM). Our modifications include Euclidean metrics instead of weighted sum formerly used for categorization of the input space. Construction of the Hidden Markov Chain is provided by turning steady weight internal synapses to associative learning synapses. Result obtained from testing on simple artificial data promises applicability in a real problem domain. We present a visualization technique of the obtained Hidden Markov Chain and the method how the results can be validated. Experiments are being performed. [ABSTRACT FROM AUTHOR]

In this paper we present an architecture for decision making of software agents that allows the agent to be-have autonomously. Our target area is computational agents — encapsulating various neural networks, genetic algorithms, and similar methods — that are expected to solve problems of different nature within an environment of a hybrid computational multi-agent system. The architecture is based on the vertically-layered and belief-desire-intention architectures. Several experiments with computational agents were conducted to demonstrate the benefits of the architecture. [ABSTRACT FROM AUTHOR]

This paper describes the application of Hybrid Intelligent Systems in a new domain: reliability of complex networks. The reliability is assessed by employing two algorithms (TREPAN and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)), both belonging to the Hybrid Intelligent Systems paradigm. TREPAN is a technique to extract linguistic rules from a trained Neural Network, whereas ANFIS is a method that combines fuzzy inference systems and neural networks. In the experiment presented, the structure function of the complex network analyzed is properly emulated by training both models on a subset of possible system configurations, generated by a Monte Carlo simulation and an appropriate Evaluation Function. Both approaches are able to successfully describe the network status through a set of rules, which allows the reliability assessment [ABSTRACT FROM AUTHOR]

Temperature modelling of a homogeneous medium, when this medium is radiated by therapeutic ultrasound, is a fundamental step in order to analyse the performance of estimators for in-vivo modelling. In this paper punctual and invasive temperature estimation in a homo-geneous medium is employed. Radial Basis Functions Neural Networks (RBFNNs) are used as estimators. The best fitted RBFNNs are selected using a Multi-objective Genetic Algorithm (MOGA). An absolute average error of 0.0084°C was attained with these estimators. [ABSTRACT FROM AUTHOR]

One of the most promising methods of interacting with small portable computing devices, such as personal digital assistants, is the use of handwriting. In order to make this communication method more natural, we proposed to visually observe the writing process on ordinary paper and to automatically recover the pen trajectory from numerical tablet sequences. On the basis of this work we developed handwriting recognition system based on visual coding and genetic algorithm. The system was applied on Arabic script. In this paper we will present the different steps of the handwriting recognition system. We focus our contribution on genetic algorithm method. [ABSTRACT FROM AUTHOR]

A novel Independent Component Analysis(ICA) algorithm is achieved, which enable to separate mixtures of symmetric and asymmetric sources with self adaptive nonlinear score functions. It is derived by using the parameterized asymmetric generalized Gaussian density (AGGD) model. Compared with conventional ICA algorithm, the proposed AGGD-ICA method can separate a wide range of signals including skewed sources. Simulations confirm the effectiveness and performance of the approach. [ABSTRACT FROM AUTHOR]

In this paper, we present a technique for tracking groups of partials in musical signals, based on networks of adaptive oscillators. We show how synchronization of adaptive oscillators can be utilized to detect periodic patterns in outputs of a human auditory model and thus track stable frequency components (partials) in musical signals. We present the integration of the partial tracking model into a connectionist system for transcription of polyphonic piano music. We provide a short overview of our transcription system and present its performance on transcriptions of several real piano recordings. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm of speaker-independent continuous Mandarin digits speech-recognition, which is based on the dynamic searching method of high-dimension space vertex cover. It doesn’t need endpoint detecting and segmenting. We construct a coverage area for every class of digits firstly, and then we put every numeric string into these coverage-areas, and the numeric string is recognized directly by the dynamic search method. Finally, there are 32 people in experiment, 16 female and 16 male, and 256 digits all together. All these digits are not learned. The correct recognition result is 218, and error recognition result is 26. Correct recognition rate is 85% [ABSTRACT FROM AUTHOR]

Thinking about the “Symbol Grounding Problem” and the brain structure of living things, the author believes that it is the best solution for generating communication in robot-like systems to use a neural network that is trained based on reinforcement learning. As the first step of the research of symbol emergence using neural network, it was examined that parallel analog communication signals are binarized in some degree by noise addition in reinforcement learning-based communication acquisition. In this paper, it is shown that two consecutive analog communication signals are binarized by noise addition using recurrent neural networks. Furthermore, when the noise ratio becomes larger, the degree of the binarization becomes larger. [ABSTRACT FROM AUTHOR]

In this work, associative memories are used for diagnostic classification of needle EMG signals. Vectors containing 44 autoregressive coefficients represent each signal and are presented as stimuli to associative memories. As the number of training stimuli increases, the method recursively updates associative memories. The obtained classification results are equivalent to the ones provided by the traditional Fisher’s discriminant, indicating the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper deals with the design of a semi-automated noise filtering approach, which receives just original noisy image and corresponding gold (user manipulated) image to learn filtering task. It tries to generate an optimized mathematical morphology procedure for image filtering by applying a genetic algorithm as an optimizer. After training and generating a morphological procedure, the approach is ready to apply the learned procedure on new noisy images. The main advantage of this approach is that it takes just one gold sample to learn filtering and does not need any prior context knowledge. Using the morphological operators makes the filtering procedure robust, effective, and computationally efficient. Furthermore, the proposed filter shows little distortion on the noise free parts of an image and it can extract objects from heavily noisy environments. Architecture of the system and details of implementation are presented. The approach feasibility is tested by well-prepared synthetic noisy images and results are given and discussed. [ABSTRACT FROM AUTHOR]

Face pose adjustment, as a loop of human face location, is very important in computer face recognition. In this paper, we present a new approach to automatic face pose adjustment on gray-scale static images with a single face. In the first stage, with the degree of mediacy make every little image, then ask for one piece of image including two eyes using match degree. And continue the nose and mouth part horizontal gray projection in small scope. Finally adjust this piece correctly. In the second stage, based on the location and the symmetry feature of eyes, the inclination angle is calculated and the face position is redressed. The experimentations show that the algorithm performs very well both in terms of rate and of efficiency. What’s more, due to the precise location of eyes, the apples of the eyes are detected. [ABSTRACT FROM AUTHOR]

We simulate binocular eye movements in reading. We introduce the 3-D edge features reconstructed from the binocular foveated vision to determine the next fixation point in reading. The next fixation point is determined statistically from the feature points in the 3-D short-term memory edge image. We show the effectiveness of simulating eyes movement based on 3-D short-term memory image to realize humanlike robots. [ABSTRACT FROM AUTHOR]

This paper proposed a new perceptual digital watermarking scheme based on ICA, SCS, the human visual system (HVS), discrete wavelet transform (DWT) and the orthogonal vector. The original gray image first is divided into 8×8 blocks, and then permuted. A 1-level DWT is applied to each 8×8 block. Each watermark bit is modulated by orthogonal vector, then the watermark is add to the original image. Finally the IDWT is performed to form the watermarked image. In the watermarking detection process the independent component analysis (ICA)-based sparse code shrinkage (SCS) technique is employed to denoise, and make using of the orthogonal vector character. By hypothetical testing, the watermark can be extracted exactly. The experimental results show that the proposed technique successfully survives image processing operations, image cropping, noise adding and the JPEG lossy compression. Especially, the scheme is robust towards image sharping and image enhancement. [ABSTRACT FROM AUTHOR]

Locking caches, providing full determinism and good performance, are a very interesting solution to replacing conventional caches in real-time systems. In such systems, temporal correctness must be guaranteed. The use of predictable components, like locking caches, helps the system designer to determine if all the tasks will meet its deadlines. However, when locking caches are used in a static manner, the system performance depends on the instructions loaded and locked in cache. The selection of these instructions may be accomplished through a genetic algorithm. This paper shows the impact of the fitness function in the final performance provided by the real-time system. Three fit- ness functions have been evaluated, showing differences in the utilisation and performance obtained. [ABSTRACT FROM AUTHOR]

This paper describes results concerning the robustness and generalization capabilities of a supervised machine learning method in detecting intrusions using network audit trails. We also evaluate the impact of kernel type and parameter values on the accuracy with which a support vector machine (SVM) performs intrusion classification. We show that classification accuracy varies with the kernel type and the parameter values; thus, with appropriately chosen parameter values, intrusions can be detected by SVMs with higher accuracy and lower rates of false alarms. Feature selection is as important for intrusion detection as it is for many other problems. We present support vector decision feature selection method for intrusion detection. It is demonstrated that, with appropriately chosen features, intrusions can be detected in real time or near real time. [ABSTRACT FROM AUTHOR]

This work describes ongoing multidisciplinary research which aims to analyse and to apply connectionist architectures to the interesting field of computer security. In this paper, we present a novel approach for Intrusion Detection Systems (IDS) based on an unsupervised connectionist model used as a method for classifying data. It is used in this special case, as a method to analyse the traffic which travels along the analysed network, detecting anomalous traffic patterns related to SNMP (Simple Network Management Protocol). Once the data has been collected and pre-processed, we use a novel connectionist topology preserving model to analyse the traffic data. It is an extension of the negative feedback network characterised by the use of lateral connections on the output layer. These lateral connections have been derived from the Rectified Gaussian distribution. [ABSTRACT FROM AUTHOR]

The data integrity in computer security is a key component of what we call trustworthy computing, and one of the most important issues in data integrity is to detect and correct error codes, which is also a crucial step in software and hardware design. Numerous methods have been recently proposed to solve legal-codes of the cyclic-code generated polynomial g(x). We think that a better approach for this purpose is to solve the legal-codes by finding the roots of the cyclic-code generated polynomial. However, as it is well known, finding roots of polynomials of high degree in the modulo-q space GF(q) is very difficult. In this paper we propose a method to solve the roots of cyclic-code generated polynomial by using evolutionary computation, which makes use of randomized searching method from biological natural selection and natural genetic system. [ABSTRACT FROM AUTHOR]

One of the main problems with text classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data [1]. Thus, there is a growing interest in exploring the combination of labeled and unlabeled data, i.e., partially labeled data [2], as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. The distributed nature of the data, usually available online, makes it a very interesting problem suited to be solved with distributed computing tools, delivered by emerging GRID computing environments. We evaluate the advantages obtained by blending supervised and unsupervised learning in a support vector machine automatic text classifier. We further evaluate the possibility of learning actively and propose a method for choosing the samples to be learned. [ABSTRACT FROM AUTHOR]

Feature selection has been an interesting issue in text categorization up to now. Previous works in feature selection often used filter model in which features, after ranked by a measure, are selected based on a given threshold. In this paper, we present a novel approach to feature selection based on multi-criteria of each feature. Instead of only one criterion, multi-criteria of a feature are used; and a procedure based on each threshold of feature selection is proposed. This framework seems to be suitable for text data and applied to text categorization. Experimental results on Reuters-21578 benchmark data show that our approach has a promising scheme and enhances the performance of a text categorization system. [ABSTRACT FROM AUTHOR]

In this paper, we present a scheme for steganalysis based on statistical correlations and machine learning. In general, digital images are highly correlated in the spatial domain and the wavelet domain; hiding data in images will affect the correlations. Different correlation features are chosen based on ANOVA (analysis of variance) in different steganographic systems. Several machine learning methods are applied to classify the extracted feature vectors. Experimental results indicate that our scheme in detecting the presence of hidden messages in several steganographic systems is highly effective. [ABSTRACT FROM AUTHOR]

We study approximation problems formulated as regularized minimization problems with kernel-based stabilizers. These approximation schemas exhibit easy derivation of solution to the problem in the shape of linear combination of kernel functions (one-hidden layer feed-forward neural network schemas). We prove uniqueness and existence of solution to the problem. We exploit the article by N. Aronszajn [1] on reproducing kernels and use his formulation of product of kernels and resulting kernel space to derive a new approximation schema — a Product Kernel Regularization Network. We present a concrete application of PKRN and compare it to classical Regularization Network and show that PKRN exhibit better approximation properties. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method to enhance classification performance based on Boosting by introducing nonlinear discriminant analysis as feature selection. To reduce the dependency between hypotheses, each hypothesis is constructed in a different feature space formed by Kernel Discriminant Analysis (KDA). Then, these hypotheses are integrated based on AdaBoost. To conduct KDA in each Boosting iteration within realistic time, a new method of kernel selection is also proposed. Several experiments are carried out for the blood cell data and thyroid data to evaluate the proposed method. The result shows that it is almost the same as the best performance of Support Vector Machine without any time-consuming parameter search. [ABSTRACT FROM AUTHOR]

Artificial Neural networks (ANNs) have found applications in a wide variety of medical problems and have proved successful for non-linear regression and classification. This paper details a novel and flexible probabilistic non-linear ANN model for the prediction of conditional survival probability of malignant melanoma patients. Hazard and probability density functions are also estimated. The model is trained using the log-likelihood function, and generalisation has been addressed. Unrestricted by assumptions that are unrealistic or parametric forms that are difficult to justify, the model thereby attains advantage over traditional statistical models. Furthermore, an estimate of the variance-covariance matrix is obtained using the asymptotic Fisher information matrix. Implemented in an Excel® spreadsheet, the model’s user-friendly design further adds to its flexibility, with much potential for use by statisticians as well as researchers. [ABSTRACT FROM AUTHOR]

In this paper, we present a neural network based system to generate an adaptive model for financial time series tracking. This kind of data is quite relevant for data quality monitoring in large databases. The proposed system uses the past samples of the series to indicate its future trend and to generate a corridor inside which the future samples should lie. This corridor is derived from an adaptive forecasting model, which makes use of the walk-forward method to take into account the most recent observations of the series and bring up to date the values of the neural model parameters. The model can serve also to manage other time series characteristics, such as the detection of irregularities. [ABSTRACT FROM AUTHOR]

The Hidden Layer Learning Vector Quantization is used to correct the prediction of multilayer perceptrons in classification of high-dimensional data. Corrections are significant for problems with insufficient training data to constrain learning. Our method, HLVQ-C, allows the inclusion of a large number of attributes without compromising the generalization capabilities of the network. The method is applied to the problem of bankruptcy prediction with excellent results. [ABSTRACT FROM AUTHOR]

Model discovery and performance surface optimization with genetic algorithm demonstrate profitability improvement with an inconclusive effect on statistical criteria. The examination of relationships between statistics used for economic forecasts evaluation and profitability of investment decisions reveals that only the ‘degree of improvement over efficient prediction’ shows robust links with profitability. If profits are not observable, this measure is proposed as an evaluation criterion for an economic prediction. Also combined with directional accuracy, it could be used in an estimation technique for economic behavior, as an alternative to conventional least squares. [ABSTRACT FROM AUTHOR]

Using a GA as a NN designing tool deals with many aspects. We must decide, among others, about: coding schema, evaluation function, genetic operators, genetic parameters, etc. This paper focuses on an efficiency of NN architecture evolution. We use two main approaches for neural network representation in the form of chromosomes: direct and indirect encoding. Presented research is a part of our wider study of this problem [1, 2]. We present the influence of coding schemata on the possibilities of evolving optimal neural network. [ABSTRACT FROM AUTHOR]

Almost all LPU algorithms rely heavily on two steps: exploiting reliable negative dataset and supplementing positive dataset. For above two steps, this paper originally proposes a two-step approach, that is, CoTrain-Active. The first step, employing CoTrain algorithm, iterates to purify the unlabeled set with two individual SVM base classifiers. The second step, adopting active-learning algorithm, further expands the positive set effectively by request the true label for the "suspect positive" examples. Comprehensive experiments demonstrate that our approach is superior to Biased-SVM which is said to be previous best. Moreover, CoTrain-Active is especially suitable for those situations where the given positive dataset P is extremely insufficient. [ABSTRACT FROM AUTHOR]

PENA (Personalized News Access) is an adaptive system for the personalized access to news. The aims of the system are to collect news from predefined news sites, to select the sections and news in the server that are most relevant for each user and to present the selected news. In this paper are described the news collection process, the techniques adopted for structuring the news archive, the creation, maintenance and update of the user model and the generation of the personalized web pages. This is a preliminary work that is based on the system that is described in [1]. [ABSTRACT FROM AUTHOR]

This paper describes the advances of our research on visualization of multi-agent systems (MAS) for purposes of analysis, monitoring and debugging. MAS are getting more complex and widely used, such analysis tools are highly beneficial in order to achieve better understanding of agents’ behaviour. Our solution is based on our originally offline visualization tools suite, which now uses a new realtime data acquisition framework. In this case we have focused on agent meta-reasoning in a MAS for planning of humanitarian relief operations. Previous tools were unable to deal with complex characteristics of these simulations. This paper describes our new approach, declares conditions and proposes visualization methods, which fulfil them. [ABSTRACT FROM AUTHOR]

Self-organizing maps, SOMs, are a data visualization technique developed to reduce the dimensions of data through the use of self-organizing neural networks. However, one of the limitations of Self Organizing Maps algorithm, is that every SOM is different and finds different similarities among the sample vectors each time the initial conditions are changed. In this paper, we propose a modification of the SOM basic algorithm in order to make the resulted mapping invariant to the initial conditions. We extend the neighborhood concept to processing units, selected in a fashionable manner, other than those commonly selected relatively to the immediate surroundings of the best matching unit. We also introduce a new learning function for the newly introduced neighbors. The modified algorithm was tested on a color classification application and performed very well in comparison with the traditional SOM. [ABSTRACT FROM AUTHOR]

Surface modeling and structure representation from unorganized sample points are key problems in many applications whose neural networks are recently starting a gradual breakthrough. Our purpose is the development of innovative self-organizing neural network architecture for surface modeling. We propose an original neural architecture and algorithm inspired by Kohonen’s self-organizing maps, based on dynamic neighborhood propagation along with an adaptive learning and repulsion process applied to a generalized mesh structure that will lead to a topological definition of the surface given as an input. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology which combines supervised learning, unsupervised learning and genetic algorithm for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised ANNs which perform an estimation of post-fault power system state. The technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 9 bus power system are presented and discussed. The analysis using such method provides accurate results and improves the effectiveness of system security evaluation. [ABSTRACT FROM AUTHOR]

A new approach to approximate the algebraic solution of systems of interval linear equations (SILE) is proposed in this paper. The original SILE problem is first transformed into an optimization problem, which is in turn solved with use of artificial neural networks and gradient-based optimization techniques. [ABSTRACT FROM AUTHOR]