This volume presents examples of how ANNs are applied in biological sciences and related areas. Chapters focus on the analysis of intracellular sorting information, prediction of the behavior of bacte
In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules fro
This book deals with new theoretical results for studying Cellular Neural Networks (CNNs) concerning its dynamical behavior. New aspects of CNNs' applications are developed for modelling of some famou
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 o
The book is a collection of invited papers on Constructive methods for Neural networks. Most of the chapters are extended versions of works presented on the special session on constructive neural netw
Iran, Slovenia, and Ukraine are among the wide range of countries represented here by scientists discussing applications the networks to chemical and biological problems, the history of such applicati
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 o
The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, object recognition, and clustering, with real-time implementations. It
This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate cou
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classi
Devoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis. It presents
Neural networks are one of the fast-growing paradigms for learning systems with a wide variety of potential applications in industry. In particular there are general results which prove the universal
