Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms

This paper presents a hybrid soft computing modeling approach, a neurofuzzy system based on rough set theory and genetic algorithms (GA). To solve the curse of dimensionality problem of neurofuzzy system, rough set is used to obtain the reductive fuzzy rule set. Both the number of condition attributes and rules are reduced. Genetic algorithm is used to obtain the optimal discretization of continuous attributes. The fuzzy system is then represented via an equivalent artificial neural network (ANN). Because the initial parameter of the ANN is reasonable, the convergence of the ANN training is fast. After the rules are reduced, the structure size of the ANN becomes small, and the ANN is not fully weight-connected. The neurofuzzy approach based on RST and GA has been applied to practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in fluid catalytic cracking unit.

[1]  Robert Susmaga,et al.  Analyzing Discretizations of Continuous Attributes Given a Monotonic Discrimination Function , 1997, Intell. Data Anal..

[2]  C. McGreavy,et al.  Data Mining and Knowledge Discovery for Process Monitoring and Control , 1999 .

[3]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[4]  Arthur K. Kordon,et al.  Robust soft sensors based on integration of genetic programming, analytical neural networks, and support vector machines , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  Lotfi A. Zadeh,et al.  Applied Soft Computing - Foreword , 2001, Appl. Soft Comput..

[6]  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.

[7]  Bull,et al.  An Overview of Genetic Algorithms: Part 2, Research Topics , 1993 .

[8]  Darrell Whitley,et al.  Genitor: a different genetic algorithm , 1988 .

[9]  Huihe Shao,et al.  Designing a soft sensor for a distillation column with the fuzzy distributed radial basis function neural network , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[10]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[11]  David Beasley,et al.  An overview of genetic algorithms: Part 1 , 1993 .

[12]  J. Galletly.  An Overview of Genetic Algorithms , 1992 .

[13]  C. Kiparissides,et al.  Inferential Estimation of Polymer Quality Using Stacked Neural Networks , 1997 .

[14]  Usama M. Fayyad,et al.  On the Handling of Continuous-Valued Attributes in Decision Tree Generation , 1992, Machine Learning.