Neural Networks for Modelling and Control of Dynamic Systems

The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, ...

 Handbook of Neural Engineering

An important new work establishing a foundation for future developments in neural engineering The Handbook of Neural Engineering provides theoretical foundations in computational neural science and engineering and current applications in wearable and i...

 Metaheuristic Procedures for Training Neural Networks

Artificial neural networks (ANNs) offer a general framework for representing non-linear mappings from several input variables to several output variables, and they can be considered as an extension of the many conventional mapping techniques. In addition ...

 New Directions in Statistical Signal Processing From Systems to Brains

Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophi...

 Intelligent Control Systems Using Soft Computing Methodologies

In recent years, intelligent control has emerged as one of the most active and fruitful areas of research and development. Until now, however, there has been no comprehensive text that explores the subject with focus on the design and analysis of biologic...

 Nonlinear Identification and Control A Neural Network Approach

The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It consists of three parts: an introduction to the fundamental principles of neural networks; several methods for nonlinear identif...

 Neural Networks in a Softcomputing Framework

Conventional model-based data processing methods are computationally expensive and require experts' knowledge for the modelling of a system; neural networks provide a model-free, adaptive, parallel-processing solution. Neural Networks in a Softcomputing F...

 Artificial Neural Networks in Real-Life Applications

Artificial Neural Networks in Real-Life Applications offers an outlook on the most recent works in the field of artificial neural networks (ANN). It includes theoretical developments of the ANN area and applications of these systems, using intelligent cha...

 Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational method...

 Neural Networks and Animal Behavior

How can we make better sense of animal behavior by using what we know about the brain? This is the first book that attempts to answer this important question by applying neural network theory. Scientists create Artificial Neural Networks (ANNs) to make mo...

 Neural Networks and Sea Time Series Reconstruction and Extreme-Event Analysis

Increasingly, neural networks are used and implemented in a wide range of fields and have become useful tools in probabilistic analysis and prediction theory. This book-unique in the literature-studies the application of neural networks to the analysis of...

 Neural Networks

Fluid and authoritative, this well-organized book represents the first comprehensive treatment of neural networks from an engineering perspective, providing extensive, state-of-the-art coverage that will expose readers to the myriad facets of neural netwo...

 Bayesian Nonparametrics Via Neural Networks

Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in...

 Neural Networks in Finance Gaining Predictive Edge in the Market

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 classical econometric methods for accuracy in forecasting, cl...

 Neural network control: theory and applications

A substantial part of the book addresses application issues in NN control, providing several interesting case studies for more application-oriented readers. Thus, while the book is written to serve as an advanced control reference on NN control for resear...

 Neural Networks for Pattern Recognition

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the prope...

 Neural networks A systematic introduction

Artificial neural networks are an alternative computational paradigm with roots in neurobiology which has attracted increasing interest in recent years. This book is a comprehensive introduction to the topic that stresses the systematic development of the...

 From Computer to Brain Foundations of Computational Neuroscience

Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Te...

 Applications and Science in Soft Computing Series: Advances in Soft Computing

The series "Advances Soft Computing" contains publications on various areas within the so-called soft computing which include fuzzy sets, rough sets, neural networks, evolutionary computations, probabilistic and evidential reasoning, multi-valued logic, a...

 The Handbook of Brain Theory and Neural Networks

Dramatically updating and extending the first edition, published in 1995, the second edition of The Handbook of Brain Theory and Neural Networks presents the enormous progress made in recent years in the many subfields related to the two great questions: ...

 Neural Networks

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most...

 Advanced Fuzzy Systems Design and Applications

This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionar...

 Neural and Adaptive Systems Fundamentals Through Simulations

This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the...

 Static and Dynamic Neural Networks From Fundamentals to Advanced Theory

Provides comprehensive treatment of the theory of both static and dynamic neural networks. Theoretical concepts are illustrated by reference to practical examples Includes end-of-chapter exercises and end-of-chapter exercises. Contents Foundations of Ne...

 Object-Oriented Neural Networks in C++

A neural network is a computational network composed of mathematically defined elements that are thought to approximate the workings of biological neurons. In situations where a decision depends on many factors, neural networks provided decisions with mor...

  Computer Vision and Fuzzy-Neural Systems

Recent advances in neural networks and fuzzy logic are transforming the field of computer vision, making it possible for computer vision applications to learn much as the brain does, and to handle the real world's imprecise visual data far more effectivel...