Artificial Neural Nets and Genetic Algorithms (häftad)
Häftad (Paperback / softback)
Antal sidor
Springer Verlag GmbH
Albrecht, Rudolf F. (ed.), Reeves, Colin R. (ed.), Steele, Nigel C. (ed.)
403 Illustrations, black and white; XIII, 737 p. 403 illus.
Artificial Neural Nets and Genetic Algorithms Proceedings of the International Conference in Innsbruck, Austria
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1 Paperback / softback
Artificial Neural Nets and Genetic Algorithms (häftad)

Artificial Neural Nets and Genetic Algorithms

Proceedings of the International Conference in Innsbruck, Austria, 1993

Häftad Engelska, 1993-05-01
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Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.
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Workshop Summary.- Artificial Neural Networks.- The Class of Refractory Neural Nets.- The Boltzmann ECE Neural Network: A Learning Machine for Estimating Unknown Probabilty Distributions.- The Functional Intricacy of Neural Networks - A Mathematical Study.- Evolving Neural Feedforward Networks.- An Example of Neural Code: Neural Trees Implemented by LRAAMs.- Kolmogorov's Theorem: From Algebraic Equations and Nomography to Neural Networks.- Output Zeroing Within a Hopfield Network.- Evolving Recurrent Neural Networks.- Speeding Up Back Propagation by Partial Evaluation.- A New Min-Max Optimization Approach for Fast Learning Convergence of Feed-Forward Neural Networks.- Evolution of Neural Net Architectures by a Hierarchical Grammar-Based Genetic System.- Interactive Classification Through Neural Networks.- Visualisation of Neural Network Operation for Improving the Performance Optimization Process.- Identification of Nonlinear Systems Using Dynamical Neural Networks: A Singular Perturbation Approach.- The Polynomial Method Augmented by Surpervised Training for Hand Printed Character Recognition.- Analysis of Electronic Nose Data Using Logical Neurons.- Neural Tree Network Based Electronic Nose.- Lime Kiln Process Identification and Control: A Neural Network Approach.- Application of Neural Networks to Automated Brain Maturation Study.- A Neuron Model for Centroid Estimation in Clustering Problems.- Systolic Pattern Recognition Based on Neural Network Algorithm.- Neural Networks Versus Image Pyramids.- Application of Neural Networks to Gradient Search Techniques in Cluster Analysis.- LVQ-Based On-Line EEG Classification.- A Scalable Neural Architecture Combining Unsupervised and Suggestive Learning.- Stability Analysis of The Separation of Sources Algorithm: Application to an Analogue Hardware.- Learning with Mappings and Input-Orderings Using Random Access Memory-Based Neural Networks.- New Preprocessing Methods for Holographic Neural Networks.- A Solution for the Processor Allocation Problem: Topology Conserving Graph Mapping by Self-Organization.- Using a Synergetic Computer in an Industrial Classification Problem.- Connectionist Unifying Prolog.- Plausible Self-Organizing Maps for Speech Recognition.- Application of Neural Networks to Fault Diagnosis for HVDC Systems.- The Hopfield and Hamming Networks Applied to the Automatic Speech Recognition of the Five Spanish Vowels.- Speaker-Independent Work Recognition with Backpropagation Networks.- A Neural Learning Framework for Advisory Dialogue Systems.- Symbolic Learning in Connectionist Production Systems.- An Evaluation of Different Network Models in Machine Vision Applications.- Combined Application of Neural Network and Artificial Intelligence Methods to Automatic Speech Recognition in a Continuous Utterance.- A Neural Network Based Control of a Simulated Biochemical Process.- Applications of Neural Networks for Filtering.- A Recurrent Neural Network for Time-Series Modelling.- The Application of Neural Sensors to Fermentation Processes.- An Application of Unsupervised Neural Networks Based Condition Monitoring System.- A Report of the Practical Application of a Neural Network in Financial Service Decision Making.- Performance Evaluation of Neural Networks Applied to Queueing Allocation Problem.- Real-Data-Based Car-Following with Adaptive Neural Control.- Genetic Algoritms.- An Adaptive Plan.- Mapping Parallel Genetic Algorithms on WK-Recursive Topologies.- Diversity and Diversification in Genetic Algorithms: Some Connections with Tabu Search.- Clique Partitioning Problem and Genetic Algorithms.- Self-Organization of Communication in Distributed Learning Classifier Systems.- Design of Digital Filters with Evolutionary Algorithms.- An Empirical Study of Population and Non-Population Based Search Strategies for Optimizing a Combinatorical Problem.- The Parallel Genetic Cellular Automata: Application to Global Function Optimization.- Optimization of Genetic Algorithms by Gene