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Fuzzy Logic: Concepts, System Design, and Applications to Industrial Informatics

Fuzzy Logic: Concepts, System Design, and Applications to Industrial Informatics

Siddhartha Bhattacharyya, Paramartha Dutta
Copyright: © 2012 |Pages: 39
ISBN13: 9781466602946|ISBN10: 1466602945|EISBN13: 9781466602953
DOI: 10.4018/978-1-4666-0294-6.ch003
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MLA

Bhattacharyya, Siddhartha, and Paramartha Dutta. "Fuzzy Logic: Concepts, System Design, and Applications to Industrial Informatics." Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions, edited by Mohammad Ayoub Khan and Abdul Quaiyum Ansari, IGI Global, 2012, pp. 33-71. https://doi.org/10.4018/978-1-4666-0294-6.ch003

APA

Bhattacharyya, S. & Dutta, P. (2012). Fuzzy Logic: Concepts, System Design, and Applications to Industrial Informatics. In M. Khan & A. Ansari (Eds.), Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions (pp. 33-71). IGI Global. https://doi.org/10.4018/978-1-4666-0294-6.ch003

Chicago

Bhattacharyya, Siddhartha, and Paramartha Dutta. "Fuzzy Logic: Concepts, System Design, and Applications to Industrial Informatics." In Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions, edited by Mohammad Ayoub Khan and Abdul Quaiyum Ansari, 33-71. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0294-6.ch003

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Abstract

The field of industrial informatics has emerged as one of the key disciplines for the purpose of intelligent management and dissemination of information in today’s world. With the advent of newer technical know-how, the subject of informative intelligence has assumed increasing importance in the industrial arena, thanks to the evolution of data intensive industry. Real world data exhibit varied amount of unquantifiable uncertainty in the information content. Conventional logic is often unable to explain the associated uncertainty and imprecision therein due to the principles of finiteness of observations and quantifying propositions employed. Fuzzy sets and fuzzy logic provide a logical framework for description of the varied amount of ambiguity, uncertainty and imprecision exhibited in real world data under consideration. The resultant fuzzy inference engine and the fuzzy logic control theory supplement the power of the framework in design of robust failsafe real life systems.

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