From the reviews:
This book teaches computational intelligence (CI) in a thorough, methodological manner that is theoretically profound and educationally oriented. ... this book is well designed for the independent student who wishes to learn the fundamentals of CI without the need for an instructor. The organization and thorough step-by-step methodology makes it an excellent startup guide for someone who wants to learn CI ... . This book is targeted at beginners, students, or professionals who wish to understand CI. (Mario Antoine Aoun, Computing Reviews, February, 2014)
The book under review is a textbook that features sub-symbolic approaches developed within the field of Artificial Intelligence ... . It can be used as a companion book for lectures, with exercises and slides to be found on the book's website. With its focus on sub-symbolic approaches, it presents a comprehensive and detailled source of information complementary to other commonly used textbooks in Artificial Intelligence that mostly focus on symbolic approaches. (Jana Koehler, zbMATH, Vol. 1283, 2014)
The book is a comprehensive treatise on computational intelligence with a focus on the underlying methodology and algorithms. ... The reader can enjoy a comprehensive and systematically arranged exposure of the material. ... The references following each chapter can serve as a list of introductory readings on the individual areas of computational intelligence. ... the reader gains a good sense of computational intelligence as an important endeavor supporting analysis and synthesis of intelligent systems. ... a useful compendium of knowledge for a broad audience. (Witold Pedrycz, Mathematical Reviews, November, 2013)Rudolf Kruse is a full professor at the Department of Computer Science of the Otto-von-Guericke University of Magdeburg, Germany, where he leads the working group on computational intelligence. Christian Moewes and Pascal Held are research assistants at the same institution. Christian Borgelt is a principal researcher at the European Centre for Soft Computing, Mieres, Spain. Frank Klawonn is a Professor at the Department of Computer Science of Ostfalia University of Applied Sciences, Wolfenbuttel, Germany. Matthias Steinbrecher is a member of the SAP Innovation Center, Potsdam, Germany.
Introduction
Part I: Neural Networks
Introduction
Threshold Logic Units
General Neural Networks
Multi-Layer Perceptrons
Radial Basis Function Networks
Self-Organizing Maps
Hopfield Networks
Recurrent Networks
Mathematical Remarks
Part II: Evolutionary Algorithms
Introduction to Evolutionary Algorithms
Elements of Evolutionary Algorithms
Fundamental Evolutionary Algorithms
Special Applications and Techniques
Part III: Fuzzy Systems
Fuzzy Sets and Fuzzy Logic
The Extension Principle
Fuzzy Relations
Similarity Relations
Fuzzy Control
Fuzzy Clustering
Part IV: Bayes Networks
Introduction to Bayes Networks
Elements of Probability and Graph Theory
Decompositions
Evidence Propagation
Learning Graphical Models