Soft computing01
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This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
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Soft computing01
- 1. Introduction to Soft Computing Lecture 1
- 2. Agenda • Introduction of softcomputing • Course outline • Recap of neural networks The student already familiar with neural network may leave after the introduction of softcomputing
- 3. Introduction (1/3) What is Softcomputing ? • The idea of softcomputing was initiated in 1981 when Lofti A.Zadeh published his first paper on soft data analysis “what is softcomputing”, softcomputing. Springer-Verlag Germany/ USA, 1997. • Zedeh, define softcomputing into one multidisciplinary system as the fusion of the fields of Fuzzy Logic, Neuro-computing, Evolutionary computing and Probabilistic Computing. • An essential aspect of soft computing is that its constituent methodologies are, for the most part, complementary and symbiotic rather than competitive and exclusive. • Softcomputing breakdown SC = EC + NC + FL + PC Softcomputing Evolutionary Neural Fuzzy Logic Probabilistic
- 4. Introduction (2/3) What is meant by fusion or hybridization ? • Hybridization create a situation where different entities cooperate advantageously for final outcome • For example, EC can be employed in the design of fuzzy-logic-based systems to improve or optimize their performance. In the reverse direction, the machinery of fuzzy logic can be employed to improve the performance of genetic algorithms.
- 5. Introduction (3/3) • Currently, the most visible systems of this hybrid type are Neuro-Fuzzy (NF) systems, Fuzzy-Genetic (FG) systems, Neural-Genetic (NG) systems, Fuzzy-Neural-Genetic (FNG) systems, Fuzzy-Probablistic (FP) systems. Other combinations are also possible. • So we are not concerned with EC, FL and NN in isolation (as in AI, ML) but hybridization is the prime concern here.
- 6. Primary Role of Individual Constituents in the Hybridization (1/2) The core of SC consist of several paradigms mainly: neural computing, evolutionary computing, probabilistic computing and fuzzy systems. • Neural computing: the importance of neurocomputing derives in large measure from the fact that NC provides effective algorithms for the purpose of system identification, classification, learning and adaptation. • Evolutionary computing: The primary contribution of evolutionary computing is a machinery for systematic random search. Such search is usually directed at finding an optimum solution to a problem. Genetic algorithms and modes of genetic computing, e.g., genetic programming, may be viewed as special cases of evolutionary computing.
- 7. Primary Role of Individual Constituents in the Hybridization (2/2) • Probabilistic computing: the primary contribution of probabilistic computing is the machinery of probability theory and the subsidiary techniques for decision-making under uncertainty. • Fuzzy logic: the primary contribution of fuzzy logic is the machinery of knowledge representation via fuzzy if-then rules and to perform logic inference like FOL with the ability to handle uncertainty and imprecision.
- 8. Hard Computing (Classical Soft Computing Artificial Intelligence) Soft (Computational Intelligence) Hard Vs Computing Prime desiderata is precision and certainty. It is Exploit tolerance for imprecision and traditional AI which is based on two principles: uncertainty. The aim is to model the firstly, represent knowledge in symbolic form remarkable abilities of human mind which (i.e. Letters, words, phrases, signs). Secondly, characteristically exploit the tolerance for search the solution with the aid of symbolic imprecision and uncertainty to e.g. understand logic (e.g. FOL). Despite success of AI for the distorted speech, sloppy handwritten, developing numerous applications (e.g. Expert expressions in natural language and drive a systems, natural language understanding, vehicle in dense traffic, etc theorem proving). It is enable to deal with advance requirement such as speech recognition, hardwritten recognition, computer vision, machine translation, learning with experience Require programs to be written Can evolve its own programs Deterministic Stochastic Require exact input Can deal with ambiguous and noisy data Produce precise answer Produce approximate answers Table: Listed in the table are some differences between hard and soft computing. The list is not exhaustive.
- 9. Structure of Soft Computing Computing Methodologies Computing Methodologies Computing Methodologies Fuzzy Systems Neural Computing Soft Computing: Hybrid Systems or Fused System Probabilistic Computing Evolutionary Computing
- 10. Definition Lofti A. Zedah, 1992: “softcomputing is an emerging approach to computing which parallel the remarkable ability of human mind to reason and learn in the environment of uncertainly and imprecision”
- 11. Course Outline (1/2) • Introduction Definition, goals and importance; recap: fuzzy computing, neural computing, genetic algorithm • Fuzzy computing Fuzzy computing: Classical set theory, crisp and non-crisp set, capturing certainty, definition of fuzzy set; graphic interpretations • Neural Computing Biological model, artificial neuron, architectures, learning methods, Taxonomy of NN systems, single and multilayer perceptrons, applications • Evolutionary Computing Genetic algorithms, taxonomy of optimization and evolution techniques: guided random search techniques, calculus-based techniques, genetic algorithms, evolutionary algorithms
- 12. Course Outline (2/2) • Associative Memory Description of AM, Examples of Auto and Hetro AM • Adaptive Resonance Theory Recap: supervised and unsupervised learning, back propagation; competitive learning, stability and plasticity dilemma, ART networks, Iterative clustering, Unsupervised ART clustering • Hybrid systems Integration of neural network, fuzzy logic and genetic algorithms, GA based back propagation network, fuzzy back propagation network, fuzzy associative memories
- 13. References • Zadeh L. A. Soft Computing and Fuzzy Logic. IEEE Software 11 (6): 48- 58, 1998. • Lofti A.Zadeh. what is softcomputing”, softcomputing. Springer-Verlag Germany/ USA, 1997. • Rajasekaran S., G. A Vijayalaksmi Pai. Neural Network, Fuzzy Logic, and Genetic Algorithms, Prentice Hall, 2005. • K. Naresh, Sinha, M. Gupta. Soft Computing and Intelligent Systems – Theory and Applications, Academic Press, 2000. • Fahreddine Karray. Soft Computing and Intelligent System Design – Theory, Tools and Applications, Addison Weslay, 2004. • Tettamanzi, Andrea, Tomassine. Soft Computing: Integrating Evolutionary, Neural and Fuzzy Systems, Springer, 2001. • J. S. R Jang, C. T. Sun. Neuro-Fuzzy and SoftComputing: A Computational Approach to Learning and Machine Intelligance, Prentice Hall, 1996.