Soft Computing Based Modeling in Intelligent Systems [electronic resource] / edited by Valentina Emilia Balas, Jnos Fodor, Annamria R. Vrkonyi-Kczy.

Soft Computing Methods for Global, Local and Personalized Modeling and Applications in Bioinformatics -- Orthosymmetry Aspects of Aggregation Functions -- MOGA Design of Neural Network Predictors of Inside Temperature in Public Buildings -- Model Based Anytime Soft Computing Approaches in Engineering Applications -- A Multi-structure Modeling Methodology -- The Generalized Dombi Operator Family and the Multiplicative Utility Function -- Multi-channel Representations of Max-Plus Algebra Based Wavelet Transform and Their Application to Video Coding -- The Fuzzy-Interpolative Methodology -- Interpolative-Type Control Solutions.

The book includes soft computing implementations of intelligent systems models. The recent popularity of fuzzy systems, neural networks and evolutionary computation, considered as related in AI, are now widely used to build intelligent systems. Professor Lotfi A. Zadeh has suggested the term "Soft Computing" for all new techniques working in these new areas of AI. Soft Computing techniques are tolerant to imprecision, uncertainty and partial truth. Due to the large variety and complexity of the domain, the constituting methods of Soft Computing are not competing for a comprehensive ultimate solution. Instead they are complementing each other, for dedicated solutions adapted to each specific problem. Hundreds of concrete applications are already available in many domains. Model based approaches offer a very challenging way to integrate a priori knowledge into procedures. Due to their flexibility, robustness, and easy interpretability, the soft computing applications will continue to have an exceptional role in our technologies.

ZDB-2-ENG