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Understanding Soft Computing Principles and Applications

Learn about Soft Computing principles and applications, favored for tolerating imprecision and uncertainty, utilizing tools like Fuzzy Logic, Neural Networks, and more for real-world problem-solving.

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Understanding Soft Computing Principles and Applications

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  1. Intelligent Systems and Soft Computing Lecture 0 What is Soft Computing Intelligent Systems and Soft Computing

  2. What is Soft Computing ? (adapted from L.A. Zadeh) Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. Intelligent Systems and Soft Computing

  3. What is Hard Computing ? • Hard computing, i.e., conventional computing, requires a precisely stated analytical model and often a lot of computation time. • Many analytical models are valid for ideal cases. • Real world problems exist in a non-ideal environment. Intelligent Systems and Soft Computing

  4. What is Soft Computing ? (continued) • The principal constituents, i.e., tools, • techniques, of Soft Computing (SC) are • Fuzzy Logic (FL), Neural Networks (NN), Support Vector Machines (SVM), Evolutionary Computation (EC), and • Machine Learning (ML) and Probabilistic Reasoning (PR) Intelligent Systems and Soft Computing

  5. Premises of Soft Computing • The real world problems are pervasively imprecise and uncertain • • Precision and certainty carry a cost Intelligent Systems and Soft Computing

  6. Guiding Principle of Soft Computing • The guiding principle of soft computing is: • Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Intelligent Systems and Soft Computing

  7. Hard Computing • Premises and guiding principles of Hard Computing are • - Precision, Certainty, and rigor. • • Many contemporary problems do not lend themselves to precise solutions such as • - Recognition problems (handwriting, speech, objects, images) • - Mobile robot coordination, forecasting, • combinatorial problems etc. Intelligent Systems and Soft Computing

  8. Implications of Soft Computing • Soft computing employs NN, EC, FL, etc, in a • complementary rather than a competitive way. • • One example of a particularly effective • combination is what has come to be known as • "neurofuzzy systems.” • • Such systems are becoming increasingly visible • as consumer products ranging from air • conditioners and washing machines to • photocopiers, camcorders and many industrial • applications. Intelligent Systems and Soft Computing

  9. Unique Property of Soft computing • Learning from experimental data • Soft computing techniques derive their power of generalization from approximating or interpolating to produce outputs from previously unseen inputs by using outputs from previous learned inputs • Generalization is usually done in a high dimensional space. Intelligent Systems and Soft Computing

  10. Current Applications using Soft Computing • handwriting recognition • speech recognition • • automotive systems and manufacturing • • image processing and data compression • • architecture • • decision-support systems • • power systems • • control • Etc. Intelligent Systems and Soft Computing

  11. Future of Soft Computing (adapted from L.A. Zadeh) • Soft computing is likely to play an especially • important role in science and engineering, but • eventually its influence may extend much • farther. • • Soft computing represents a significant paradigm shift in the aims of computing • a shift which reflects the fact that the human mind, unlike present day computers, possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain and lacking in categoricity. Intelligent Systems and Soft Computing

  12. AI and Soft Computing: A Different Perspective • AI-Expert Systems: predicate logic and symbol manipulation techniques User Global Database Inference Engine Question User Interface Explanation Facility KB: • Fact • rules Response Knowledge Acquisition Knowledge Engineer Human Expert Expert Systems

  13. AI and Soft Computing ANN Learning and adaptation Fuzzy Set Theory Knowledge representation Via Fuzzy if-then RULE Genetic Algorithms Systematic Random Search

  14. AI and Soft Computing ANN Learning and adaptation Fuzzy Set Theory Knowledge representation Via Fuzzy if-then RULE Genetic Algorithms Systematic Random Search AI Symbolic Manipulation

  15. AI and Soft Computing cat Animal? cat cut Neural character recognition knowledge

  16. Conventional AI • Focuses on attempt to mimic human intelligent behavior by expressing it in language forms or symbolic rules • Manipulates symbols on the assumption that such behavior can be stored in symbolically structured knowledge bases (physical symbol system hypothesis)

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