Artificial Neural Nets and Genetic Algorithms

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Proceedings of the International Conference in Alès, France, 1995

ISBN:	3211826920
ISBN 13:	9783211826928
Autor:	Pearson, David W/Steele, Nigel C/Albrecht, Rudolf F
Verlag:	Springer Verlag GmbH
Umfang:	xv, 522 S., 339 s/w Illustr., 522 p. 339 illus.
Erscheinungsdatum:	05.04.1995
Auflage:	1/1995
Produktform:	Kartoniert
Einband:	Kartoniert

InhaltsangabeWorkshop Summary.- Invited Talk.- Kohonen neural networks for machine and process condition monitoring.- Plenary Session.- Process modelling and control with neural networks: present status and future directions.- Session A1: Classification.- A genetic algorithm for multicriteria inventory classification.- Optimizing classifiers for handwritten digits by genetic algorithms.- DCS: A promising classifier system.- Application of neural networks for classification of temperature distribution patterns.- Session B1: Genetic Algorithms and Combinatorial Optimisation.- Combination of genetic algorithms and CLP in the vehicle-fleet scheduling problem.- Minimum cost topology optimisation of the COST 239 European optical network.- Timetabling using genetic algorithms.- Resolution of cartographic layout problem by means of improved genetic algorithms.- Using genetic algorithms to solve the radio link frequency assignment problem.- Session C1: Learning and Training.- A transformation for implementing efficient dynamic backpropagation neural networks.- AA1*: a dynamic incremental network that learns by discrimination.- Non-supervised sensory-motor agents learning.- Functional equivalence and genetic learning of RBF networks.- Teaching relaxation labeling processes using genetic algorithms.- VLSI optimal neural network learning algorithm.- Session A2: Applications in Biology and Biotechnology.- Using of neural-network and expert-system for imissions prediction.- The costing of process vessels using neural networks.- Neural network modelling of fermentation taking into account culture memory.- Brain electrographic state detection using combined unsupervised and supervised neural networks.- Session B2: Genetic and Neural Theory Combined.- From the chromosome to the neural network.- CAM-Brain: the evolutionary engineering of a billion neuron artificial brain by 2001 which grows/evolves at electronic speeds inside a cellular automata machine (CAM).- A perfect integration of neural networks and evolutionary algorithms.- G-LVQ, a combination of genetic algorithms and LVQ.- Evolving neural network structures: an evaluation of encoding techniques.- Session C2: Failure Detection and Identification.- Application of radial basis function networks to fault diagnosis for a hydraulic system.- Optimally robust fault diagnosis using genetic algorithms.- Development of a neural-based diagnostic system to control the ropes of mining shifts.- Lime kiln fault detection and diagnosis by neural networks.- Investigations into the use of wavelet transformations as input into neural networks for condition monitoring.- Session A3: Image, Motion and Recognition.- Genetic algorithm for neurocomputer image recognition.- Feature map architectures for pattern recognition: techniques for automatic region selection.- Adaptive genetic algorithms for multi-point path finding in artificial potential fields.- Spatio-temporal mask learning: application to speech recognition.- Artificial neural networks for motion estimation.- Advanced neural networks methods for recognition of handwritten characters.- Session B3: Genetic Algorithms Theory.- The use of a variable length chromosome for permutation manipulation in genetic algorithms.- Theoretical bounds for genetic algorithms.- An argument against the principle of minimum alphabet.- Heterogeneous co-evolving parasites.- Typology of boolean functions using Walsh analysis.- Session C3: Neural Networks Theory.- Artificial neural networks for nonlinear projection and exploratory data analysis.- Generic back-propagation in arbitrary feedforward neural networks.- From prime implicants to modular feedforward networks.- Hierarchical backward search method: a new classification tree using preprocessing by multilayer neural network.- Adaptation algorithms for 2-D feedforward neural networks.- Selecting the best significant fragment to the incremental heteroassociative neural network (RHI).- Session A4: Image, Motion and Recognition.- An orth

Artikelnummer: 5744086 Kategorie: Informatik

Beschreibung

Beschreibung

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 subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.