Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing. First, the authors lay down the preliminary neuroscience background for engineers. The book also presents an overview of the role of correlation in the human brain as well as in the adaptive signal processing world; unifies many well-established synaptic adaptations (learning) rules within the correlation-based learning framework, focusing on a particular correlative learning paradigm, ALOPEX; and presents case studies...

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

Neural Network research studies how computers can be designed to emulate many of the logical and intelligent functions of the brain. The principles behind how the brain work are closely related to a statistical technique known as the Principal Component Analysis (PCA). PCA neural networks are systems that use this classical statistical technique to process information. Understanding biological perceptual systems is of great importance to engineers and computer scientists who wish to use this knowledge to develop artificial perceptual systems. This book examines the relationship between the...

An introduction to a new design for nonlinear control systems— backstepping— written by its own architects.

This innovative book breaks new ground in nonlinear and adaptive control design for systems with uncertainties. Introducing the recursive backstepping methodology, it shows— for the first time— how uncertain systems with severe nonlinearities can be successfully controlled with this new powerful design tool.

Communicative and accessible at a level not usually present in research texts, Nonlinear and Adaptive Control Design can be used as either a...

An in-depth examination of intelligent approaches to increasing the accuracy of a variety of system components. Utilizing a unified, adaptive, inverse approach, the book offers electrical, mechanical, chemical, aeronautical and computer engineers methods for controlling many of the -hard- nonlinearities of frequently-employed control systems such as dead-zone, backlash and hysteresis. Discusses such nonlinearities at both the input and output points of a linear part and within both continuous time designs and discrete time designs.

The technologies of neural networks and fuzzy systems have undergone extensive growth in recent years. However, each field has developed independently. Each has its own nomenclature and symbology. On the surface there is little in common between the fields, yet their areas of application overlap considerably. Furthermore, there is a mutually beneficial synergistic relationship between the two technologies. This text demonstrates how these two technologies can be integrated to achieve good results. It contains numerous detailed examples, many using graphical methods, to illustrate principles...

A complete, up-to-date textbook on an increasingly important subject

Robust Systems Theory and Applications covers both the techniques used in linear robust control analysis/synthesis and in robust (control-oriented) identification. The main analysis and design methods are complemented by elaborated examples and a group of worked-out applications that stress specific practical issues: nonlinearities, robustness against changes in operating conditions, uncertain infinite dimensional plants, and actuator and sensor limitations. Designed expressly as a textbook for master's and...

• Edited by the original inventor of the technology.
• Includes contributions by the foremost experts in the field.
• The only book to cover these topics together.

A systematic and unified presentation of the fundamentals of adaptive control theory in both continuous time and discrete time

Today, adaptive control theory has grown to be a rigorous and mature discipline. As the advantages of adaptive systems for developing advanced applications grow apparent, adaptive control is becoming more popular in many fields of engineering and science. Using a simple, balanced, and harmonious style, this book provides a convenient introduction to the subject and improves one's understanding of adaptive control theory.

Adaptive Control Design and...

The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures
Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes-through a learning process and information storage involving interconnection...