A robust learning algorithm based on support vector regression and robust fuzzy cerebellar model articulation controller |
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Authors: | Zne-Jung Lee |
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Affiliation: | (1) Dept. of Information Management, No. 1, Huafan Rd. Shihtin Hsiang, Taipei Hsien, 223, Taiwan |
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Abstract: | For real-world applications, the obtained data are always subject to noise or outliers. The learning mechanism of cerebellar
model articulation controller (CMAC), a neurological model, is to imitate the cerebellum of human being. CMAC has an attractive
property of learning speed in which a small subset addressed by the input space determines output instantaneously. For fuzzy
cerebellar model articulation controller (FCMAC), the concept of fuzzy is incorporated into CMAC to improve the accuracy problem.
However, the distributions of errors into the addressed hypercubes may cause unacceptable learning performance for input data
with noise or outliers. For robust fuzzy cerebellar model articulation controller (RFCMAC), the robust learning of M-estimator
can be embedded into FCMAC to degrade noise or outliers. Meanwhile, support vector machine (SVR) is a machine learning theory
based algorithm which has been applied successfully to a number of regression problems when noise or outliers exist. Unfortunately,
the practical application of SVR is limited to defining a set of parameters for obtaining admirable performance by the user.
In this paper, a robust learning algorithm based on support SVR and RFCMAC is proposed. The proposed algorithm has both the
advantage of SVR, the ability to avoid corruption effects, and the advantage of RFCMAC, the ability to obtain attractive properties
of learning performance and to increase accurate approximation. Additionally, particle swarm optimization (PSO) is applied
to obtain the best parameters setting for SVR. From simulation results, it shows that the proposed algorithm outperforms other
algorithms. |
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Keywords: | Robust learning SVR CMAC Fuzzy CMAC Particle swarm optimization |
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