Michel Verleysen – författare

Nowadays, real-time applications of Fuzzy Logic in different domains are being increasingly reported. ASIC-based analog hardware becomes an interesting solution for these kinds of applications because it benefits from: savings on silicon surface and power consumption, readily accomplishment with strict timing constraints and cost-effective production. This book focuses in-depth on the VLSI CMOS implementation and application of programmable analog Fuzzy Logic Controllers following a mixed-signal philosophy. This is to say, signals are processed in the analog domain whereas programmability is achieved by means of standard digital memories.

This approach highlights the following crucial aspects: *The comprehensive study and analysis of the main analog fuzzy operators: Fuzzy Membership Functions, T-Norm, T-CoNorm and Defuzzifier circuits. *The study and development of mixed-signal Fuzzy Controllers architectures targeting the requirements for different applications. *The fabrication and test of full-ended demonstrators. *The partial fabrication and test of a prototype corresponding to a real-time Fuzzy Logic application in the field of Signal Processing.

Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods.

This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples.

The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings.

The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.