This is a new, completely revised, updated and enlarged edition of the author's Ergebnisse vol. 46: "Spin Glasses: A Challenge for Mathematicians" in two volumes (this is the 2nd volume). In the eighties, a group of theoretical physicists introduced several models for certain disordered systems, called "spin glasses." These models are simple and rather canonical random structures, of considerable interest for several branches of science (statistical physics, neural networks and computer science). The physicists studied them by non-rigorous methods and predicted spectacular behaviors. This book introduces in a rigorous manner this exciting new area to the mathematically minded reader. It requires no knowledge whatsoever of any physics. The present Volume II contains a considerable amount of new material, in particular all the fundamental low-temperature results obtained after the publication of the first edition.
Industrial production is one of the most basic human activities indispensable to the economic activity. Due to its complexity, production is not very well understood and modeled as opposed to traditional fields of inquiry such as physics. This book aims at enhancing rigorous understanding of a particular area of production, that of analysis and optimization of production lines and networks using discrete event models and simulation. To our knowledge, this is the first book treating this subject from the point of view mentioned above. We have arrived at the realization that discrete event models and simulation provide perhaps the best tools to model production lines and networks for a number of reasons. Analysis is precise but demands enormous computational resources, usually unavailable in practical situations. Brute force simulation is also precise but slow when quick decisions are to be made. Approximate analytical models are fast but often unreliable as far as accuracy is concerned. The approach of the book, on the other hand, combines speed and accuracy to an exceptional degree in most practical applications.
This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.
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