Convergence performance analysis of an adaptive kernel width MCC algorithm
A fixed kernel width in MCC algorithm imposes a trade-off among robustness, convergence rate and steady-state accuracy. With a variable kernel width, the adaptive kernel width MCC (AMCC) algorithm can improve the learning speed of the MCC algorithm especially when the initial weight vector is far aw...
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| Veröffentlicht in: | International journal of electronics and communications Jg. 76; S. 71 - 76 |
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| Sprache: | Englisch |
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01.06.2017
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| ISSN: | 1434-8411, 1618-0399 |
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| Abstract | A fixed kernel width in MCC algorithm imposes a trade-off among robustness, convergence rate and steady-state accuracy. With a variable kernel width, the adaptive kernel width MCC (AMCC) algorithm can improve the learning speed of the MCC algorithm especially when the initial weight vector is far away from the optimal weight vector. In this paper, the steady-state excess mean square error (EMSE) of the AMCC algorithm is studied based on energy conservation relation. In addition, a novel convergence measure called initial convergence rate is introduced to evaluate the convergence speed at the beginning of the learning. Simulation experiments are carried out to verify the theoretical analysis and confirm the desirable performance of the AMCC algorithm in several different non-Gaussian noise environments. |
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| AbstractList | A fixed kernel width in MCC algorithm imposes a trade-off among robustness, convergence rate and steady-state accuracy. With a variable kernel width, the adaptive kernel width MCC (AMCC) algorithm can improve the learning speed of the MCC algorithm especially when the initial weight vector is far away from the optimal weight vector. In this paper, the steady-state excess mean square error (EMSE) of the AMCC algorithm is studied based on energy conservation relation. In addition, a novel convergence measure called initial convergence rate is introduced to evaluate the convergence speed at the beginning of the learning. Simulation experiments are carried out to verify the theoretical analysis and confirm the desirable performance of the AMCC algorithm in several different non-Gaussian noise environments. |
| Author | Zhao, Jihong Chen, Badong Principe, Jose C. Qu, Hua Wang, Weihua |
| Author_xml | – sequence: 1 givenname: Weihua surname: Wang fullname: Wang, Weihua organization: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China – sequence: 2 givenname: Jihong surname: Zhao fullname: Zhao, Jihong organization: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China – sequence: 3 givenname: Hua surname: Qu fullname: Qu, Hua organization: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China – sequence: 4 givenname: Badong surname: Chen fullname: Chen, Badong email: chenbd@mail.xjtu.edu.cn organization: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China – sequence: 5 givenname: Jose C. surname: Principe fullname: Principe, Jose C. organization: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China |
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| Keywords | Maximum correntropy criterion Adaptive kernel width Convergence performance analysis |
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