Artificial Neural Networks: International Workshop IWANN '91, Granada, Spain, September 17-19, 1991. Proceedings - Tapa blanda

Sinopsis

Cooperative computing and neural networks.- Neural Net's theory - The specifications of a computational model of memory and information processing in decision-making.- Chaotic neural networks and associative memory.- Nonequilibrium model of neural networks.- A modified algorithm for self-organizing maps based on the Schrödinger equation.- Neural network modelling by means of networks of finite automata.- Adaptive optimization of neural algorithms.- Neural networks with hysteresis type of nonlinearity exhibit global optimization property.- Stability measurement criterion for neural networks of competitive learning.- On the power of networks of majority functions.- Using quadratic perceptrons to reduce interconnection density in multilayer neural networks.- Always trying to write an equation for the brain.- Transformation of control signals for saccadic eye movements.- On the semantics of morphogenesys in photoreceptors.- Implementing a "psychophysical" pattern classifier in a decrementing network.- Contributions of Neural Net's Theory to the understanding of Psychopathological productions in Schizophrenia.- Backpropagation growing networks: Towards local minima elimination.- Methods for encoding in multilayer feed-forward neural networks.- Learning algorithm for feed-forward neural networks with discrete synapses.- Synthesis of adaptive memories with neural networks.- Minimally disturbing learning.- Fuzzy-neunet: A non standard neural network.- Decrementing hamming and Bayesian neural networks: Analog implementations and relative performance.- Dynamic thresholds and attractor neural networks.- Use of genetic algorithms in neural networks definition.- Simulated evolution of modular networks.- Computational experiments with Boltzmann Machines.- An adaptive resonance theory architecture for the automatic recognition of on-line handwritten symbols of a mathematical editor.- An experimental design advisor and neural network analysis package.- Extending an object oriented concurrent logic language for neural network simulations.- Application and implementation of neural networks in microelectronics.- Cmos implementation of a cellular neural network with dynamically alterable cloning templates.- Systolic implementation of hopfield networks of arbitrary size.- Vlsi fully connected neural networks for the implementation of other topologies.- Backpropagation multilayer perceptron: A modular implementation.- Toroidal neural network processor: Multiple learning algorithm support.- Cmos implementation of synapse matrices with programmable analog weights.- Analog VLSI synapse matrix with enhanced stochastic computations.- Cmos continuous BAM with on chip learning.- An integrated circuit for artificial neural networks.- An application of neural networks to natural scene segmentation.- An approach to isolated word recognition using multilayer perceptrons.- The use of multilayer perceptrons in isolated word recognition.- Continuous speech recognition with the connectionist viterbi training procedure: A summary of recent work.- Recurrent neural networks for speech recognition.- A speech recognition system that integrates neural nets and HMM.- Comparison of neural networks and conventional techniques for automatic recognition of a multilingual speech database.- Optimization problems on concurrent testing solved by neural networks.- Application of high-order hopfield neural networks to the solution of diophantine equations.- Self-organizing feature maps and their application to digital coding of information.- Neural networks as error correcting systems in digital communications.- Application of vector quantization algorithms to protein classification and secondary structure computation.- Application of the LVQ neural method to a stellar catalogue.- Neural network design for mobile robot control following a contour.- A supervisory technique to apply neural networks in control.- Autonomous controller tuning by using a neural network.- Neural netw

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