Neural Networks Theory

• Κύριος συγγραφέας: Galushkin, Alexander I. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Θέματα: Engineering.; Artificial intelligence.; Statistical physics.; Dynamical systems.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).; Statistical Physics, Dynamical Systems and Complexity.

 "Neural Networks Theory is a major contribution to the neural networks literature. It is a treasure trove that should be mined by the thousands of researchers and practitioners worldwide who have not previously had access to the fruits of Soviet and Russian neural network research. Dr. Galushkin is to be congratulated and thanked for his completion of this monumental work; a book that only he could write. It is a major gift to the world." Robert Hecht Nielsen, Computational Neurobiology, University of California, San Diego "Professor Galushkin’s monograph has many unique features that in totality make his work an important contribution to the literature of neural networks theory. He and his publisher deserve profuse thanks and congratulations from all who are seriously interested in the foundations of neural networks theory, its evolution and current status." Lotfi Zadeh, Berkeley, Founder of Fuzziness "Professor Galushkin, a leader in neural networks theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization, concepts that are solidly grounded in Russian tradition. His theory is expansive: covering not just the traditional topics such as network architecture, it also addresses neural continua in function spaces. I am pleased to see his theory presented in its entirety here, for the first time for many, so that the both theory he developed and the approach he took to understand such complex phenomena can be fully appreciated." Sun-Ichi Amari, Director of RIKEN Brain Science Institute RIKEN. 

 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.