A robust learning algorithm based on support vector regression and robust fuzzy cerebellar model articulation controller
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 sub...
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| Published in: | Applied intelligence (Dordrecht, Netherlands) Vol. 29; no. 1; pp. 47 - 55 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Boston
Springer US
01.08.2008
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
| Online Access: | Get full text |
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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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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] |
| Author | Lee, Zne-Jung |
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| CitedBy_id | crossref_primary_10_1007_s10489_009_0163_1 crossref_primary_10_1007_s10489_009_0203_x crossref_primary_10_1007_s10462_023_10485_5 crossref_primary_10_1007_s10489_009_0185_8 crossref_primary_10_1016_j_ijar_2021_02_006 crossref_primary_10_3390_app11219827 crossref_primary_10_1016_j_asoc_2010_05_028 |
| Cites_doi | 10.1016/j.aca.2004.12.024 10.1109/72.279188 10.1109/91.971730 10.1177/003754979205800504 10.1109/TSMCB.2003.810447 10.1016/j.asoc.2004.01.007 10.1016/j.ins.2006.03.010 10.1016/j.ejor.2005.07.024 10.1109/83.536888 10.1109/72.641451 10.1109/72.105424 10.1016/S0893-6080(05)80021-8 10.1109/TPWRS.2005.846106 10.1109/TEVC.2004.826067 10.1109/TEVC.2005.857610 10.1109/72.105415 10.1016/S0893-6080(03)00169-2 10.1016/j.cie.2005.01.018 10.1109/TSMCC.2005.860570 10.1109/TSMCB.2005.861067 10.1109/3477.718518 10.1007/978-1-4757-2440-0 10.1115/1.3426922 10.1115/1.3426923 10.1109/IMTC.2003.1207926 10.1109/ICNN.1995.488968 10.1023/A:1008385515068 |
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| Keywords | Fuzzy CMAC CMAC Robust learning Particle swarm optimization SVR |
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| SubjectTerms | Algorithms Artificial Intelligence Cerebellar model articulation controller CMAC Computer Science Fuzzy Fuzzy logic Fuzzy set theory Learning Machines Manufacturing Mechanical Engineering Noise Processes Studies |
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| Title | A robust learning algorithm based on support vector regression and robust fuzzy cerebellar model articulation controller |
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