Автор: Bishop, Christopher M. Название: Neural Networks for Pattern Recognition ISBN: 0198538642 ISBN-13(EAN): 9780198538646 Издательство: Oxford Academ Рейтинг: Цена: 12293 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Providing a comprehensive account of neural networks from a statistical perspective, this book emphasizes on pattern recognition, which represents the area of greatest applicability for neural networks in contemporary times.
Описание: This book is the first major text to encompass the wide diversity of geophysical applications of artificial neural networks (ANNs) and fuzzy logic (FZ). Each chapter, written by internationally-renowned experts in their field, represents a specific geophysical application, ranging from first-break picking and trace editing encountered in seismic exploration, through well-log lithology determination, to electromagnetic exploration and earthquake seismology. The book offers a well-balanced division of contributions from industry and academia, and includes a comprehensive, up-to-date bibliography covering all major publications in geophysical applications of ANNs and FZ. A special feature of this volume is the preface written by Professor Fred Aminzadeh, eminent authority in the field of artificial intelligence and geophysics. The enclosed CD-ROM contains full colour figures and searchable files, as well as short biographies of the editors.
Автор: Slavova A. Название: Cellular Neural Networks: Dynamics and Modelling ISBN: 140201192X ISBN-13(EAN): 9781402011924 Издательство: Springer Рейтинг: Цена: 22204 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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 famous nonlinear partial differential equations arising in biology, genetics, neurophysiology, physics, ecology, etc. The analysis of CNNs' models is based on the harmonic balance method well known in control theory and in the study of electronic oscillators. Such phenomena as hysteresis, bifurcation and chaos are studied for CNNs. The topics investigated in the book involve several scientific disciplines, such as dynamical systems, applied mathematics, mathematical modelling, information processing, biology and neurophysiology. The reader will find comprehensive discussion on the subject as well as rigorous mathematical analyses of networks of neurons from the view point of dynamical systems. The text is written as a textbook for senior undergraduate and graduate students in applied mathematics. Providing a summary of recent results on dynamics and modelling of CNNs, the book will also be of interest to all researchers in the area.
Описание: The two volume set LNCS 3173/3174 constitutes the refereed proceedings of the International Symposium on Neural Networks, ISNN 2004, held in Dalian, China in August 2004.The 329 papers presented were carefully reviewed and selected from more than 800 submissions. The papers span the entire scope of neural computing and its applications; they are organized in 11 major topical parts on theoretical analysis; learning and optimization; support vector machines; blind source separation, independent component analysis, and principal component analysis; clustering and classification; robotics and control; telecommunications; signal image, and time series analysis; biomedical applications; detection, diagnosis, and computer security; and other applications.
Описание: Modern aerospace, automotive, nautical, industrial, microsystem-assembly and robotic systems are becoming more and more complex. High-performance vehicles no longer have built-in error safety margins, but are inherently unstable by design to allow for more flexible maneuvering options. With the push towards better performance in terms of greater accuracy and faster speed of response, control demands are increasing. The combination of highly nonlinear dynamics, relaxed static stability, and tight performance specifications places increasing demands on the design of feedback systems for control. Current control system design techniques have difficulty in meeting these demands.
Автор: Alba Enrique, Marti Rafael Название: Metaheuristic Procedures for Training Neural Networks ISBN: 0387334157 ISBN-13(EAN): 9780387334158 Издательство: Springer Рейтинг: Цена: 23016 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides successful implementations of metaheuristic methods for neural network training. This book gives the basic principles and fundamental ideas that allow the readers to create successful training methods on their own. It covers the concepts, methods, and tools of the important area of ANNs within the realm of continuous optimization.
Автор: Khare Mukesh, Nagendra S.M. Shiva Название: Artificial Neural Networks in Vehicular Pollution Modelling ISBN: 3540374175 ISBN-13(EAN): 9783540374176 Издательство: Springer Рейтинг: Цена: 24979 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial neural networks (ANNs), which are parallel computational models, comprising of interconnected adaptive processing units (neurons) have the capability to predict accurately the dispersive behavior of vehicular pollutants under complex environmental conditions. This book aims at describing step-by-step procedure for formulation and development of ANN based VP models considering meteorological and traffic parameters. The model predictions are compared with existing line source deterministic/statistical based models to establish the efficacy of the ANN technique in explaining frequent dispersion complexities in urban areas.00
Описание: The three volume set LNCS 3496/3497/3498 constitutes the refereed proceedings of the Second International Symposium on Neural Networks, ISNN 2005, held in Chongqing, China in May/June 2005.The 483 revised papers presented were carefully reviewed and selected from 1.425 submissions. The papers are organized in topical sections on theoretical analysis, model design, learning methods, optimization methods, kernel methods, component analysis, pattern analysis, systems modeling, signal processing, image processing, financial analysis, control systems, robotic systems, telecommunication networks, incidence detection, fault diagnosis, power systems, biomedical applications, industrial applications, and other applications.
Описание: The three volume set LNCS 3496/3497/3498 constitutes the refereed proceedings of the Second International Symposium on Neural Networks, ISNN 2005, held in Chongqing, China in May/June 2005.The 483 revised papers presented were carefully reviewed and selected from 1.425 submissions. The papers are organized in topical sections on theoretical analysis, model design, learning methods, optimization methods, kernel methods, component analysis, pattern analysis, systems modeling, signal processing, image processing, financial analysis, control systems, robotic systems, telecommunication networks, incidence detection, fault diagnosis, power systems, biomedical applications, industrial applications, and other applications.
Описание: The three volume set LNCS 3496/3497/3498 constitutes the refereed proceedings of the Second International Symposium on Neural Networks, ISNN 2005, held in Chongqing, China in May/June 2005.The 483 revised papers presented were carefully reviewed and selected from 1.425 submissions. The papers are organized in topical sections on theoretical analysis, model design, learning methods, optimization methods, kernel methods, component analysis, pattern analysis, systems modeling, signal processing, image processing, financial analysis, control systems, robotic systems, telecommunication networks, incidence detection, fault diagnosis, power systems, biomedical applications, industrial applications, and other applications.
Описание: This book constitutes the refereed proceedings of the 8th International Workshop on Artificial Neural Networks, IWANN 2005, held in Vilanova i la GeltrГє, Barcelona, Spain in June 2005.The 150 revised papers presented – including the contribution of three invited speakers – were carefully reviewed and selected from 240 submissions for inclusion in the book and address the following topics: mathematical and theoretical methods, evolutionary computation, neurocomputational inspired models, learning and adaptation, radial basic functions structures, self-organizing networks and methods, support vector machines, cellular neural networks, hybrid systems, neuroengineering and hardware implementations, pattern recognition, perception and robotics and applications in a broad variety of fields.
Автор: Mastebroek H.A., Vos J.E. Название: Plausible Neural Networks for Biological Modelling ISBN: 0792371925 ISBN-13(EAN): 9780792371922 Издательство: Springer Рейтинг: Цена: 20789 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book has the unique intention of returning the mathematical tools of neural networks to the biological realm of the nervous system, where they originated a few decades ago. It aims to introduce, in a didactic manner, two relatively recent developments in neural network methodology, namely recurrence in the architecture and the use of spiking or integrate-and-fire neurons. In addition, the neuro-anatomical processes of synapse modification during development, training, and memory formation are discussed as realistic bases for weight-adjustment in neural networks. While neural networks have many applications outside biology, where it is irrelevant precisely which architecture and which algorithms are used, it is essential that there is a close relationship between the network's properties and whatever is the case in a neuro-biological phenomenon that is being modelled or simulated in terms of a neural network. A recurrent architecture, the use of spiking neurons and appropriate weight update rules contribute to the plausibility of a neural network in such a case. Therefore, in the first half of this book the foundations are laid for the application of neural networks as models for the various biological phenomena that are treated in the second half of this book. These include various neural network models of sensory and motor control tasks that implement one or several of the requirements for biological plausibility.
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