An Overview of Soft Computing

Soft computing, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximations. In effect, the role model for soft computing is the human mind. Soft computing is based on techniques such as fuzzy logic, genetic algorithms, artificial neural networks, machine learning, and expert systems. Although soft computing theory and techniques were first introduced in 1980s, it has now become a major research and study area in automatic control engineering. The techniques of soft computing are nowadays being used successfully in many domestic, commercial, and industrial applications. With the advent of the low-cost and very high performance digital processors and the reduction of the cost of memory chips it is clear that the techniques and application areas of soft computing will continue to expand. This paper gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques.

[1]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[2]  Puja Gupta,et al.  An Introduction of Soft Computing Approach over Hard Computing , 2013 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[4]  M. Crucianu,et al.  Multi-step-ahead Prediction with Neural Networks : a Review , 2002 .

[5]  Y. Dote,et al.  Intelligent control, power electronic systems , 1999, IEEE Power Engineering Review.

[6]  F. Russo,et al.  Fuzzy systems in instrumentation: fuzzy signal processing , 1995, Proceedings of 1995 IEEE Instrumentation and Measurement Technology Conference - IMTC '95.

[7]  Nirwan Ansari,et al.  Neural Networks in Telecommunications , 1994, Springer US.

[8]  J. Buckley,et al.  Fuzzy neural networks: a survey , 1994 .

[9]  Seppo J. Ovaska,et al.  Friction compensation in servo motor systems using neural networks , 1999, SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269).

[10]  Rajkumar Roy,et al.  Advances in Soft Computing: Engineering Design and Manufacturing , 1998 .

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[13]  Marco Saerens,et al.  Neural controller based on back-propagation algorithm , 1991 .

[14]  A. T. C. Goh,et al.  Back-propagation neural networks for modeling complex systems , 1995, Artif. Intell. Eng..

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[17]  Y. Komori.  A neural fuzzy training approach for continuous speech recognition improvement , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Witold Pedrycz,et al.  Fuzzy sets in pattern recognition: Methodology and methods , 1990, Pattern Recognit..

[19]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.