From the Publisher:
"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems."
-- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. 
In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.
Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. 
Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

Foundations of neural networks, fuzzy systems, and knowledge engineering

Ten years of genetic fuzzy systems: current framework and new trends

The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

Computational Intelligence: An Introduction

Although fuzzy systems demonstrated their ability to solve different kinds of problems in various applications, there is an increasing interest on augmenting them with learning capabilities. Two of the most successful approaches to hybridise fuzzy systems with adaptation methods have been made in the realm of soft computing: neuro-fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The article focuses on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field.

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Design of fuzzy logic controller for smart base isolation system using genetic algorithm

This paper presents a new approach to genetic-based machine learning (GBML). The new approach utilizes mechanisms of genetic recombination in bacterial genetics, and the authors have called the new approach “Nagoya approach”. The Nagoya approach is efficient in improving local portions of chromosomes. An obstacle avoidance problem for a mobile robot is simulated using the Nagoya approach, and complex fuzzy rules are found.

A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules - Proposal of Nagoya Approach -

This paper proposes a fuzzy abductive inference method to realize a creative thinking support system. The fuzzy logic is applied to Peng and Reggia's (1990) abductive inference for handling degrees of manifestations. Application of the new method to a diagnostic problem is shown and the effectiveness of the proposed method is demonstrated by comparing the inferred results with those by the conventional fuzzy abduction. >

A study on fuzzy abductive inference

This paper proposes a fuzzy abductive inference with degrees of manifestation. The fuzzy logic is applied to Peng and Reggia's abductive inference for handling the manifestation degrees. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. A learning algorithm for updating the fuzzy causations and t-conorm parameter is also presented in this paper. Application of the new method to a diagnostic problem is shown and the effectiveness of the proposed method is demonstrated.

A proposal of abductive inference with degrees of manifestations

A new approach to genetic based machine learning for efficient local improvement and its application to a graphic problem

Proposes a query expansion method using fuzzy abductive inference for a creative thinking support system. This query expansion method for information retrieval is useful, because unexpected related information can probably be obtained by adding new words to the query. The fuzzy abductive inference is the inference method based on Peng and Reggia's (1990) abductive inference. This method infers nonredundant combinations of concept words for the query. This paper also studies how to update the weights of concept-term relationships by the steepest descent method.

Yujiro Miyata

Papers

A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules - Proposal of Nagoya Approach -

A study on fuzzy abductive inference

A proposal of abductive inference with degrees of manifestations

A new approach to genetic based machine learning for efficient local improvement and its application to a graphic problem

Query expansion using fuzzy abductive inference for creative thinking support system