Fuzzy-Neural Networks Based on Subtractive Clustering and its Applications in Soft Sensor

Yuan Yuan Sui

doi:10.4028/www.scientific.net/AMM.239-240.1516

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240Fuzzy-Neural Networks Based on Subtractive...

Fuzzy-Neural Networks Based on Subtractive Clustering and its Applications in Soft Sensor

Abstract:

A soft sensor modeling method is presented in this paper，it selects optimal fuzzy rules by tuning the radius of a subtractive cluster center to generate a T-S fuzzy model. The radius of a cluster center is adjusted to select optimal number of fuzzy rules, to acquire a fuzzy model with perfect generalization capability. Then, the parameter is fine-tuned by means of a hybrid gradient descent (GD) and least-squares estimation (LSE) approach. Finally, the method is used to model a PDU Naphtha’s Dry Point, simulation results show that it can determine the optimal model quickly and achieve satisfactory prediction precision.

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Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

1516-1521

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.1516

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Online since:

December 2012

Authors:

Yuan Yuan Sui

Keywords:

Radius of a Cluster Center, Soft Sensor, Subtractive Clustering, T-S Fuzzy Model

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