Neural Networks Theory

Softcover Nachdruck of hardcover 1. Auflage 2007
ISBN: 978-3-642-08006-7
Verlag: Springer

Buch, Englisch, 396 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 1086 g

This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.

Zielgruppe

Research

Autoren/Hrsg.

Galushkin, Alexander I.

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure

Inhaltsverzeichnis

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.