Advanced fuzzy systems design and applications

Despite the tremendous advances in performance enabled by modern architectures, there are always new applications and demands arising that require ever-increasing capabilities. Keeping up with these demands requires a deep-seated understanding of contemporary architectures in concert with a fundamental understanding of basic principles that allows one to anticipate what will be possible over the system's lifetime. Advanced Computer Architectures focuses on the design of high performance supercomputers with balanced coverage of the hardware, software structures, and application characteristics. This book is a timeless distillation of underlying principles punctuated by real-world implementations in popular current and past commercially available systems. It briefly reviews the basics of uniprocessor architecture before outlining the most popular processing paradigms, performance evaluation, and cost factor considerations. This builds to a discussion of pipeline design and vector processors, data parallel architectures, and multiprocessor systems. Rounding out the book, the final chapter explores some important current and emerging trends such as Dataflow, Grid, biology-inspired, and optical computing. More than 220 figures, tables, and equations illustrate the concepts presented. Based on the author's more than thirty years of teaching and research, Advanced Computer Architectures endows you with the tools necessary to reach the limits of existing technology, and ultimately, to break them. About the Book: This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with the help of fuzzy logic. On the one hand, knowledge that is understandable to human beings can be extracted from data using evolutionary and learning methods by maintaining the interpretability of the generated fuzzy rules. On the other hand, a priori knowledge like expert knowledge and human preferences can be incorporated into evolution and learning, taking advantage of the knowledge representation capability of fuzzy rule systems and fuzzy preference models. Several engineering application examples in the fields of intelligent vehicle systems, process modeling and control and robotics are presented. With all the material available in the field of artificial intelligence (AI) and soft computing-texts, monographs, and journal articles-there remains a serious gap in the literature. Until now, there has been no comprehensive resource accessible to a broad audience yet containing a depth and breadth of information that enables the reader to fully understand and readily apply AI and soft …