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[...]... Algorithms in Neural Networks 25 I.10.6 Investigation of Neural Network Adaptation Algorithms 27 I.10.7 Multilayer Neural Networks with Flexible Structure 28 I.10.8 Informative Feature Selection in Multilayer Neural Networks 28 I.10.9 Investigation of Neural Network Reliability 29 I.10.10 Neural Network... Average Risk Function Criterion for Neural Networks with N* Output Channels (Neuron Layer) Implementation of the Minimum Average Risk Function Criterion for Multilayer Neural Networks Development of Closed-Loop Neural Networks of Non-Stationary Patterns Development of Closed-Cycle Adjustable Neural Networks with Cross and Backward... the neural processing element implementation The following neural computer elemental base can be considered as a second-priority one: Custom-designed digital CMOS neural chips; Optoelectronic GaAs neural chip; Analogous CMOS neural chips I.8 · Some Remarks Concerning the Neural Computer Elemental Base The following neural computer elemental base can be considered as a third-priority one: System of neural. .. of the neural network input signal) I.7 Neural Computer Classification According to the common opinion of designers, neural networks have a much wider field of implementation than any other implementations of parallelism concepts due to the fact that the property of a large massive parallelism in the case of neural networks is embedded inside of them Figure I.5 shows a structure of the main neural. .. 31 A.1 Theory of Multilayer Neural Networks 31 A.2 Neural Computer Implementation 32 A.3 Neural Computer Elemental Base 32 I.1 I.2 I.3 I.4 I.5 I.6 I.7 I.8 I.9 Part I · The Structure of Neural Networks ... 11.5 Typical Input Signal of Multilayer Neural Networks Literature 220 220 220 221 221 222 12 Analysis of Closed-Loop Multilayer Neural Networks 12.1 Problem Statement for the Synthesis of the Multilayer Neural Networks Adjusted in the Closed Cycle ... Dynamics for the Neural Networks of Particular Form for the Non-Stationary Pattern Recognition 12.4 Dynamics of the Three-Layer Neural Network in the Learning Mode 12.5 Investigation of the Particular Neural Network with Backward Connections 12.6 Dynamics of One-Layer Neural Networks in the... the Multilayer Neural Networks with Fixed Structure 14.3 Selection of the Initial Space Informative Features Using Multilayer Neural Networks with Sequential Algorithms of the First-Layer Neuron Adjustment 14.4 Neuron Number Minimization 14.5 About the Informative Feature Selection for Multilayer Neural Networks in the... Problem Solution in the Neural Network Logical Basis 17.5 Multilayer Neural Networks with Flexible Structure 17.6 Neural Network with Fixed Structure 17.6.1 Generation of the Input Signal of the Neural Network 17.6.2 The Multilayer Neural Network Output... evidence of the neural networks I.2 · Position of Neural Computers in the Set of Large-Powered Computing Facilities Control theory The complexities of nonlinear dynamic control system synthesis are well known In the case of neural computers, these complexities are partially overcome when a special case of a control object is taken This object is well formalized and represents a multilayer neural network . optimization theory and other disciplines, and results have been outstanding. Russia’s contribution to neural networks theory is yet another example. Professor Galushkin, a leader in neural networks theory. played a pivotal role in the development of neural networks theory and its applications in the Soviet Union ever since. Development of neural networks theory in the Soviet Union paralleled and,. literature of neural networks theory. He and his publisher deserve profuse thanks and congratulations from all who are seri- ously interested in the foundations of neural networks theory, its evolution