Intorduction to soft computing

Introduction to soft computingNirmala Shinde

SyllabusIntroduction to soft ComputingFuzzy Set TheoryFuzzy SystemsHybrid System

BooksPrinciple of Soft computing:, sivanandam, wileyNeural Network, fuzzy logic, and genetic algorithm, Rajasekaran, Printice hall

ContentAI & Soft computingFrom conventional AI to Computational IntelligenceWhat is soft computing?Problem Solving TechniquesHard Vs Soft ComputingOverview of techniques in soft computing

AI and SoftcomputingAI: predicate logic and symbol manipulation techniques

User InterfaceInference EngineExplanation FacilityKnowledge AcquisitionKB: FactrulesGlobalDatabaseKnowledgeEngineerHumanExpert

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Response

Expert SystemsUser

AI and SoftcomputingANNLearning and adaptationFuzzy Set TheoryKnowledge representationViaFuzzy if-then RULEGenetic AlgorithmsSystematic Random Search

AI and SoftcomputingANNLearning and adaptationFuzzy Set TheoryKnowledge representationViaFuzzy if-then RULEGenetic AlgorithmsSystematic Random SearchAISymbolic Manipulation

AI and Softcomputing

catcutknowledge

Animal?cat

Neural characterrecognition

From Conventional AI to Computational IntelligenceConventional AI:Focuses on attempt to mimic human intelligent behavior by expressing it in language forms or symbolic rulesManipulates symbols on the assumption that such behavior can be stored in symbolically structured knowledge bases (physical symbol system hypothesis)

From Conventional AI to Computational IntelligenceIntelligent Systems

Sensing Devices(Vision)Natural Language ProcessorMechanicalDevices

Perceptions

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TaskGeneratorKnowledgeHandlerDataHandler

KnowledgeBase

MachineLearningInferencing(Reasoning)Planning

What is soft computing ?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.

Soft Computing is the fusion of methodologies designed to model and enable solutions to real world problems, which are not modeled or too difficult to model mathematically.

The aim of Soft Computing is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve close resemblance with human like decision making.

ContSoft Computing is a term used in computer science to refer to problems in whose solutions are unpredictable, uncertain and between 0 and 1.

Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost.

The idea of soft computing was initiated in 1981 BY Lotfi A. Zadeh.

Cont

According to Prof. Zadeh:"...in contrast to traditional hard computing, soft computing exploits the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution-cost, and better rapport with reality

en.wikipedia.org/wiki/Soft_computing :Soft computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally-hard tasks such as the solution of NP-complete problems, for which an exact solution cannot be derived in polynomial time.

Goals of Soft ComputingTo develop intelligent machines to provide solutions to real world problems, which are not modelled or too difficult to model mathematically.

To exploit the tolerance for approximation, uncertainty, imprecision and partial truth in order to achieve close resemblance with human like decision making.

Well suited for real world problems where ideal solutions are not there.

Contimprecision the model features (quantities) are not the same as that of the real ones, but close to them.uncertainty we are not sure that the features of the model are the same as that of the entity (belief).Approximate Reasoning the model features are similar to the real ones, but not the same.The guiding principle of soft computing is to exploit these tolerance to achieve tractability, robustness and low solution cost. The role model for soft computing is the human mind.

Problem Solving Techniques

Symbolic LogicReasoning Traditional Numerical Modeling and Search

Approximate Reasoning

Functional Approximationand Randomized SearchHARD COMPUTINGSOFT COMPUTING

Precise ModelsApproximate Models

Hard Computing Vs Soft Computing

Hard computing Soft Computingrequires precisely state analyticmodeltolerant of imprecision,uncertainty, partial truth and approximationbased on binary logic, crisp system, numerical analysis and crisp softwarebased on fuzzy logic, neural sets, andprobabilisticreasoning has the characteristics of precisionhas the characteristics of approximationrequires programs to be writtencan evolve its own programsuses two-valued logic.can use multivalued or fuzzy logicis deterministic.incorporates stochasticity requires exact input data.can deal with ambiguous and noisy datais strictly sequentialallows parallel computationsproduces precise answers.can yield approximate answers

Overview of techniques in soft computingNeural NetworkFuzzy LogicGenetic AlgorithmHybrid Systems

Neural NetworkDARPA Neural Network Study (1988, AFCEA International Press, p. 60):

... a neural network is a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes.

ContAccording to Haykin (1994), p. 2: A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects:

Knowledge is acquired by the network through a learning process.

Interneuron connection strengths known as synaptic weights are used to store the knowledge

ContAccording to Nigrin (1993), p. 11: A neural network is a circuit composed of a very large number of simple processing elements that are neurally based. Each element operates only on local information. Furthermore each element operates asynchronously; thus there is no overall system clock.

According to Zurada (1992): Artificial neural systems, or neural networks, are physical cellular systems which can acquire, store and utilize experiential knowledge.

Multidisciplinary view of neural network

Fuzzy LogicOrigins: Multivalued Logic for treatment of imprecision and vagueness

1930s: Post, Kleene, and Lukasiewicz attempted to represent undetermined, unknown, and other possible intermediate truth-values.

1937: Max Black suggested the use of a consistency profile to represent vague (ambiguous) concepts.

1965: Zadeh proposed a complete theory of fuzzy sets (and its isomorphic fuzzy logic), to represent and manipulate ill-defined concepts.

Genetic Algorithm

Definition of Genetic AlgorithmThe genetic algorithm is a probabalistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation.

Steps Involved in Genetic AlgorithmThe genetic algorithms follow the evolution process in the nature to find the better solutions of some complicated problems. Foundations of genetic algorithms are given in Holland (1975) and Goldberg (1989) books. Genetic algorithms consist the following steps:InitializationSelectionReproduction with crossover and mutation

Selection and reproduction are repeated for each generation until a solution is reached.During this procedure a certain strings of symbols, known as chromosomes, evaluate toward better solution.

Hybrid SystemsHybrid systems enables one to combine various soft computing paradigms and result in a best solution. The major three hybrid systems are as follows:

Hybrid Fuzzy Logic (FL) Systems

Hybrid Neural Network (NN) Systems

Hybrid Evolutionary Algorithm (EA) Systems

Neural Networks