Neural Networks Theory

Uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. This title details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis.

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.