Soft Computing Introduction

Introduction Soft Computing refers to a consortium of computational methodologies like fuzzy logic, neural networks, genetic algorithms etc All having their roots in the Artificial Intelligence Artificial Intelligence is an area of computer science concerned with designing intelligent computer systems. Systems that exhibit the characteristics we associate with intelligence in human behavior.

Soft Computing was introduced by Lotfi A zadeh of the university of California, Berkley, U.S.A The soft computing differs from hard computing in its tolerance to imprecision, uncertainty and partial truth. Soft Computing has high Machine Intelligent Quotient [MIQ] It is the processes of analyzing, organizing and converting data into knowledge is defined as the structured information acquired and applied to remove ignorance and uncertainty about a specific task pertaining to the intelligent machine.

Hybrid Systems x Neuro Fuzzy y Neuro Genetic Algorithms z Genetic Algorithms Fuzzy k Neuro Fuzzy Genetic Algorithms Neural Networks Genetic Algorithms y k x z Fuzzy Logic

Neural Networks It is simplified models of the biological nervous system and therefore have drawn their motivation from the kind of computing performed by a human brain. It is as highly interconnected networks of a large number of processing elements called neurons is an architecture inspired by the brain. It can be massively parallel , so it exhibits parallel distributed processing. Neural networks learn by examples

To be trained with known examples of a problem to acquire knowledge about it. This trained network can be put to effective use in solving ‘unknown’ or ‘untrained’ instances of the problem. Supervised Learning A ‘teacher ‘ is assumed to be present during the learning process. The network aims to minimize the error between the target output presented by the teacher and the computed output, to achieve better performance.

Unsupervised Learning There is no teacher present to hand over the desired output and the network therefore tries to learn by itself organizing the input instances of the problem. Neural Networks Architectures Classification Single Layer Feed forward Networks Multi Layer Feed forward Networks Recurrent Networks

Stock Market Prediction Neural Networks Application Areas Pattern Recognition Image Processing Data Compression Forecasting Optimization Stock Market Prediction

Neural Networks Systems Backpropagation Network Perceptron ADALINE [Adaptive Linear Element] Associative Memory Boltzmann Machine Adaptive Resonance Theory Self-organizing feature map Hopfield network

Fuzzy Logic It try to capture the way humans represent and reason with real world knowledge in the face of uncertainty. Uncertainty could arise due to generality, vagueness, ambiguity, chance or incomplete knowledge The capability of fuzzy set to express gradual transitions from membership to non-membership and vice versa has a broad utility.

Operations on fuzzy sets Union Intersection Subsethood Composition of relations Fuzzy Logic Multivalued truth values True Absolutely True Fairly True False Absolutely False Partly False

● Fuzzy logic washing machines These machines offer the advantages of performance, productivity, simplicity, productivity, and less cost. Sensors continually monitor varying conditions inside the machine and accordingly adjust operations for the best wash results. Typically, fuzzy logic controls the washing process, water intake, water temperature, wash time, rinse performance, and spin speed. This optimizes the life span of the washing machine. More sophisticated machines weigh the load , advise on the required amount of detergent, assess cloth material type and water hardness, and check whether the detergent is in powder or liquid form. Some machines even learn from past experience, memorizing programs and adjusting them to minimize running costs.

Genetic Algorithms It initiated and developed in the early 1970 by John Holland are unorthodox search and optimization algorithms, which mimic some of the processes of natural evolution. GAs perform random searches through a given set of alternatives with the aim of finding the best alternative with respect to the given criteria of goodness. These criteria are required to be expressed in terms of an objective function which is usually referred to as a fitness function.

Genetic Operations Reproduction Cross over Mutation Inversion Dominance Deletion Duplication Translocation Segregation Speciation Migration Sharing Mating

things in order to create useful machines that can do work for humans. Application: Robotics Robotics involves human designers and engineers trying out all sorts of things in order to create useful machines that can do work for humans. Each robot's design is dependent on the job or jobs it is intended to do, so there are many different designs out there. GAs can be programmed to search for a range of optimal designs and components for each specific use, or to return results for entirely new types of robots that can perform multiple tasks and have more general application. GA-designed robotics just might get us those nifty multi-purpose, learning bots we've been expecting any year now .

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