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- Soft Computing - SC
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**Anna University -**- Computer Science Engineering
- B.Tech
- 7 Topics
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www.csetube.in Introduction Basics of Soft Computing Soft Computing Topics (Lectures 01, 02, 03, 04, 05, 06 6 hours) Slides 03-06 1. Introduction What is Soft computing? Definitions, Goals, and Importance. 07-28 2. Fuzzy Computing Fuzzy Set: Classical set theory, Crisp & Non-crisp set, Capturing uncertainty, Definition of fuzzy set; Graphic Interpretations : Fuzzy set Small, Prime numbers, Universal space, Empty; Fuzzy operations : Complement, Union, Intersection. in Inclusion, Equality, Comparability, 29-39 e. 3. Neural Computing ub Biological model, Information flow in neural cell; Artificial neuron: et Functions, Equation, Elements, Single and Multi layer Perceptrons. 40-49 cs 4. Genetic Algorithms w. What are GAs ? Why GAs ? Mechanics of Biological evolution; Artificial ww Evolution and Search Optimization: Taxonomy of Evolution & Search optimization - Enumerative, Calculus-based and Guided random search techniques, Evolutionary algorithms (EAs). 50-53 5. Associative Memory Description of AM; Examples of Auto and Hetero AM. 54-58 6. Adaptive Resonance Theory Definitions of ART and other types of learning; ART : Description, Model functions , Training, and Systems. 7. Applications of Soft Computing 59 60-61 8. References 02 www.csetube.in

www.csetube.in Introduction Basics of Soft Computing What is Soft Computing ? • The idea of soft computing was initiated in 1981 when Lotfi A. Zadeh published his first paper on soft data analysis “What is Soft Computing”, Soft Computing. Springer-Verlag Germany/USA 1997.]. • Zadeh, defined Soft Computing into one multidisciplinary system as the fusion of the fields of Fuzzy Logic, Neuro-Computing, Evolutionary and Genetic Computing, and Probabilistic Computing. in • Soft Computing is the fusion of methodologies designed to model and difficult to model mathematically. e. enable solutions to real world problems, which are not modeled or too ub • The aim of Soft Computing is to exploit the tolerance for imprecision, in order to achieve et uncertainty, approximate reasoning, and partial truth cs close resemblance with human like decision making. = ww SC Soft Computing Zadeh 1981 EC w. • The Soft Computing – development history = EC + NN + FL Evolutionary Computing Neural Network Fuzzy Logic Rechenberg 1960 McCulloch 1943 Zadeh 1965 GP + ES + EP + GA Evolutionary Computing Genetic Programming Evolution Strategies Evolutionary Programming Genetic Algorithms Rechenberg 1960 Koza 1992 Rechenberg 1965 Fogel 1962 Holland 1970 03 www.csetube.in

www.csetube.in SC - Definitions 1. Introduction To begin, first explained, the definitions, the goals, and the importance of the soft computing. Later, presented its different fields, that is, Fuzzy Computing, Neural Computing, Genetic Algorithms, and more. Definitions of Soft Computing (SC) Lotfi A. Zadeh, 1992 : “Soft Computing is an emerging approach to computing which parallel the remarkable ability of the human mind to reason and learn in a environment of uncertainty and imprecision”. The Soft Computing consists of several computing paradigms mainly : Fuzzy Systems, Neural Networks, and Genetic Algorithms. in • Fuzzy set : for knowledge representation via fuzzy If – Then rules. e. • Neural Networks : for learning and adaptation ub • Genetic Algorithms : for evolutionary computation et These methodologies form the core of SC. cs Hybridization of these three creates a successful synergic effect; that is, hybridization creates a situation where different entities cooperate ww w. advantageously for a final outcome. Soft Computing is still growing and developing. Hence, a clear definite agreement on what comprises Soft Computing has not yet been reached. More new sciences are still merging into Soft Computing. 04 www.csetube.in

www.csetube.in SC - Goal Goals of Soft Computing Soft Computing is a new multidisciplinary field, to construct new generation of Artificial Intelligence, known as Computational Intelligence. • The main goal of Soft Computing is to develop intelligent machines to provide solutions to real world problems, which are not modeled, or too difficult to model mathematically. • Its aim is to exploit the tolerance for Approximation, Uncertainty, Imprecision, and Partial Truth in order to achieve close resemblance with human like decision making. Approximation : here the model features are similar to the real ones, but not the same. here we are not sure that the features of the model are in Uncertainty : here the model features (quantities) are not the same ub Imprecision : e. the same as that of the entity (belief). as that of the real ones, but close to them. ww w. cs et 05 www.csetube.in

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