Michel Verleysen – författare

However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. 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.

Fuzzy logic is a computational paradigm capable of modelling the own uncertainness of human beings. Fuzzy reasoning is nothing else than a fuzzy logic-based formalism for encoding human knowledge or common sense in a numerical framework. Indeed, the mathematical concepts on which fuzzy logic is supported are very easy to understand. In a fuzzy controller, human experience is codified by means of linguistic if-then rules, which compute control actions upon given conditions. fuzzy logic has been applied to problems that are difficult to solve mathematically. One of its main advantages lies in the fact that it offers a straightforward methodology for modelling and controlling non-linear systems, which are difficult to face by means of conventional techniques. Nowadays, real-time applications of fuzzy logic in different domains are being increasingly reported. ASIC-based analogue hardware becomes an interesting solution for these kinds of applications because it benefits from: savings on silicon surface and power consumption, readily accomplished with strict timing constraints and cost-effective production.This book focuses in-depth on the VLSI CMOS implementation and application of programmable analogue fuzzy logic controllers following a mixed-signal philosophy. This is to say, signals are processed in the analogue domain whereas programmability is achieved by means of standard digital memories. This approach highlights the following aspects: the comprehensive study and analysis of the main analogue fuzzy operators; fuzzy membership functions, T-Norm, T-CoNorm and fefuzzifier 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; and the partial fabrication and test of a prototype corresponding to a real-time fuzzy logic application in the field of signal processing. Throughout this text, the authors emphasize and demonstrate the powerfulness of fuzzy logic for the synthesis of analogue non-linear systems in a systematic approach. However, beyond the scope of fuzzy logic applications, the analysis of the circuits presented herein is wide-ranging.These circuits can also be employed in different kinds of applications in the field of analogue signal processing such as neural networks, non-linear and linear adaptive filtering, analogue computation, and so forth.

However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. 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.

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.

Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classi?cation, prototypes, or Hebbian learning, with a large variety of di?erent, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray pro?les for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry thepromiseofane?cient,cheap,andautomaticgatheringoftonsofhigh-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standingofbiologicalsystems,reliablediagnosisoffaults,andtherapyofdiseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the speci?cs of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.

Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis.Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty.Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance.Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.