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Splitting K-means generated Neural Fuzzy System with Support Vector Regression | IEEE Conference Publication | IEEE Xplore

Splitting K-means generated Neural Fuzzy System with Support Vector Regression


Abstract:

This paper proposes a Splitting K-means generated Neural Fuzzy System with Support Vector Regression (SKNFS SVR). The consequent layer in SKNFS-SVR is a Takagi-Sugeno-Kan...Show More

Abstract:

This paper proposes a Splitting K-means generated Neural Fuzzy System with Support Vector Regression (SKNFS SVR). The consequent layer in SKNFS-SVR is a Takagi-Sugeno-Kang (TSK)-type consequent. For structure learning, a splitting K-means algorithm clusters the input training data and determines the rule number. For parameter learning, a linear support vector regression (SVR) algorithm is proposed to tune free parameters in the consequent part. The motivation for using SVR for parameter learning is to improve the SKNFS-SVR generalization ability. This paper demonstrates the capabilities of SKNFS-SVR by conducting simulations in clean and noisy function approximations. This paper also compares simulation results from the SKNFS-SVR with Gaussian kernel-based SVR.
Date of Conference: 27-30 June 2011
Date Added to IEEE Xplore: 01 September 2011
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Conference Location: Taipei, Taiwan

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