Alexander I. Galushkin

Neural Networks Theory (eBook, PDF)

• Format: PDF

Produktbeschreibung

This volume details more than 40 years of Soviet and Russian neural network research. The systematized methodology of neural networks synthesis is presented in the monograph which is intending for works in different regimes: teaching, self-teaching (clusterization), teaching with a teacher of finite qualification and other regimes. The different methods of multilayer networks synthesis to the different optimization criterions are presented: minimum average risk function, minimum average risk function with different limits to its components. It is a treasure trove that should be mined by the thousands of researchers and practitioners worldwide in neural networks who have not previously had access to this work.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 396
• Erscheinungstermin: 29. Oktober 2007
• Englisch
• ISBN-13: 9783540481256
• Artikelnr.: 37369330

Inhaltsangabe

The Structure of Neural Networks.- Transfer from the Logical Basis of Boolean Elements "AND, OR, NOT" to the Threshold Logical Basis.- Qualitative Characteristics of Neural Network Architectures.- Optimization of Cross Connection Multilayer Neural Network Structure.- Continual Neural Networks.- Optimal Models of Neural Networks.- Investigation of Neural Network Input Signal Characteristics.- Design of Neural Network Optimal Models.- Analysis of the Open-Loop Neural Networks.- Development of Multivariable Function Extremum Search Algorithms.- Adaptive Neural Networks.- Neural Network Adjustment Algorithms.- Adjustment of Continuum Neural Networks.- Selection of Initial Conditions During Neural Network Adjustment - Typical Neural Network Input Signals.- Analysis of Closed-Loop Multilayer Neural Networks.- Synthesis of Multilayer Neural Networks with Flexible Structure.- Informative Feature Selection in Multilayer Neural Networks.- Neural Network Reliability and Diagnostics.- Neural Network Reliability.- Neural Network Diagnostics.- Conclusion.- Methods of Problem Solving in the Neural Network Logical Basis.