Madan M. Gupta – författare

The field of soft computing is emerging from the cutting edge research over the last ten years devoted to fuzzy engineering and genetic algorithms. The subject is being called soft computing and computational intelligence. With acceptance of the research fundamentals in these important areas, the field is expanding into direct applications through engineering and systems science.This book cover the fundamentals of this emerging filed, as well as direct applications and case studies. There is a need for practicing engineers, computer scientists, and system scientists to directly apply "fuzzy" engineering into a wide array of devices and systems.

This text examines the use of analytical tools developed for studying uncertainty analysis in engineering, control systems, and the sciences. It is the work of 38 contributors who have each written chapters on developed analytical methods - fuzzy logic, neural networks, simulation, and Bayesian techniques - and have applied them to uncertainty phenomena arising out of information and knowledge problems in the fields of engineering and the sciences. The book is divided into the following parts : part I reports the theoretical studies on uncertainty types, models and measures; part II reviews the applications of uncertain theoretical tools to engineering systems; part III describes the methodologies of fuzzy-neural data analysis and forecasting; part IV presents two chapters on fuzzy-neuro systems; and part V describes the methodologies for fuzzy decision making and optimization and their computational methods. The editors also provide a concluding chapter on uncertainty and uncertainty modelling.

One of the attractions of fuzzy logic is its utility in solving many real engineering problems. The major obstacles in building a real intelligent machine involve dealing with random disturbances, processing large amounts of imprecise data, interacting with a dynamically changing environment, and coping with uncertainty. Neural-fuzzy techniques help one to solve many of these problems.

Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.