This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet. The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.

The First Conference on Engineering Probability in Flood Defense was orga nized by the Department of Mathematics and Informatics of the Delft U niver sity of Technology and the Department of Industrial Engineering and Opera tions Research of the University of California at Berkeley, and was held on June 1,2 1995 in Delft. Groups at Berkeley and Delft were both deeply engaged in modeling deterioration in civil structures, particularly flood defense structures. The plans for the conference were well under way when the dramatic floods in The Netherlands and California in the winter of 1994-1995 focused world attention on these problems. The design of civil engineering structures and systems is essentially an example of decision making under uncertainty. Although the decision making part of the process is generally acknowledged, the uncertainty in variables and param eters in the design problem is less frequently recognized. In many practical design procedures the uncertainty is concealed behind sharp probabilistic de sign targets like 'once in a thousand years' combined with a standardized use of safety factors. The choice of these probabilistic design targets, however, is based on an assessment of the uncertainty of the variable under consideration, and on its assessed importance. The value of the safety factor is governed by similar considerations. Standard practice is simply accu ulated experience and engineering judgment. In light of the great number of civil engineering structures that function suc-. cessfully, one may say that this standard practice has proven itself broadly satisfactory."

The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty - i.e., "type-2" fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems - from type-1 to interval type-2 to general type-2 - in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.

This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet. The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.

The First Conference on Engineering Probability in Flood Defense was orga nized by the Department of Mathematics and Informatics of the Delft U niver sity of Technology and the Department of Industrial Engineering and Opera tions Research of the University of California at Berkeley, and was held on June 1,2 1995 in Delft. Groups at Berkeley and Delft were both deeply engaged in modeling deterioration in civil structures, particularly flood defense structures. The plans for the conference were well under way when the dramatic floods in The Netherlands and California in the winter of 1994-1995 focused world attention on these problems. The design of civil engineering structures and systems is essentially an example of decision making under uncertainty. Although the decision making part of the process is generally acknowledged, the uncertainty in variables and param eters in the design problem is less frequently recognized. In many practical design procedures the uncertainty is concealed behind sharp probabilistic de sign targets like 'once in a thousand years' combined with a standardized use of safety factors. The choice of these probabilistic design targets, however, is based on an assessment of the uncertainty of the variable under consideration, and on its assessed importance. The value of the safety factor is governed by similar considerations. Standard practice is simply accu ulated experience and engineering judgment. In light of the great number of civil engineering structures that function suc-. cessfully, one may say that this standard practice has proven itself broadly satisfactory."

Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.

This is a timely review of the mechanisms underlying presynaptic control of synaptic transmission and the role they play in sensory and motor behavior. It will be of particular interest to neuroscientists studying synaptic transmission or sensorimotor control.

The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty - i.e., "type-2" fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems - from type-1 to interval type-2 to general type-2 - in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.

For those who have grown tired of Christmas commercialism, who feel Santa Claus and red-nosed reindeer do not project the true Christmas spirit, this book may be the answer. With little-known classics by old and new masters of the genre, this unique anthology of Christmas stories, songs, and poems has long been a seasonal favorite of old and young alike.