Soft computing and intelligent systems design - [Book review]

The textbook “Soft Computing and Intelligent Systems Design, Theory, Tools and Applications” by Fakhreddine Karray and Clarence W. de Silva represents a comprehensive and cohesive treatment of the state-of-theart consortium of soft-computing methodologies and their potential integration, from both the analytical and the practical perspectives. The textbook thoroughly details the technical aspects of its topics to better serve practicing professionals, yet it does not sacrifice clarity of presentation and simplicity in style, which makes it also appealing for students and novice researchers in the textbook’s related fields. The large number of illustrative examples, end-ofchapter problems, and solved case studies in various engineering applications make the textbook an excellent choice for a wide range of courses in interdisciplinary engineering fields. The content structure of the book suits courses in areas such as fuzzy logic, neural networks, evolutionary computing, machine intelligence, and intelligent control. The textbook is organized into four main parts. The first part presents soft computing and its applications including intelligent control. The second part deals with the various types of connectionist modeling techniques and their applications. Part three discusses evolutionary computing algorithms and their synergistic integration with neural networks. Part four demonstrates the use of the a priori-discussed techniques through a number of worked case studies taken from real-world applications in various engineering disciplines. Chapters 1 through 3 comprise Part 1. Chapter 1 elegantly introduces machine intelligence and outlines tools of soft computing and their merits for the design of a wide range of intelligent systems. Chapter 2 uses realworld examples to motivate the use of fuzzy set theory before it tackles the fundamental aspects and the theoretical background of the topic. The discussed concepts are illustrated with a number of examples to help the reader grasp the theory behind the discussed concepts. Chapter 3 is devoted to discussing the major aspects of fuzzy logic control. It also explores different strategies in designing fuzzy logic controllers while enumerating their properties and popular applications. Part 2 on neural networks and their integration into dynamic fuzzy models covers Chapters 4 through 7. Chapter 4 provides a general introduction to artificial neural networks. Chapter 5 details the major classes of neural networks with a particular emphasis on multilayer perceptrons, radial basis function networks, Kohonen’s self-organizing networks, and Hopfield networks. The chapter also presents an extensive review on the applications of these types of neural networks in a wide spectrum of industrial applications. Chapter 6 introduces the concept of dynamic neural networks and illustrates their use in dynamic processes and chaos-time series prediction. Chapter 7 bridges Parts 1 and 2 by revealing different cutting-edge methodologies for the synergistic integration of fuzzy logic and neural networks. Part 3 consists of Chapter 8, which starts with an introduction on evolutionary computing. It then lays the fundamentals of genetic algorithms including the schema theorem. The rest of the chapter is devoted to reviewing selected techniques for integrating genetic algorithms with neural networks and fuzzy logic. The chapter is concluded with a set of