| WNN中的改进PSO算法及参数初始化 |
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| 引用本文： | 岑翼刚,孙德宝,李宁.WNN中的改进PSO算法及参数初始化[J].华中科技大学学报(自然科学版),2006,34(8):43-45. |
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| 作者姓名： | 岑翼刚 孙德宝 李宁 |
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| 作者单位： | 华中科技大学,控制科学与工程系,湖北,武汉,430074 |
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| 摘 要： | 利用粒子群（PSO）算法替代BP算法对小波神经网络（WNN）进行训练，针对局部极小值问题提出了改进的PSO算法，即判断当粒子陷人局部极小时将其重新初始化，并对小波的平移和伸缩参数的初始化进行了研究，避免了网络的盲目搜索，减少了迭代次数．通过非线性函数逼近的仿真结果表明，上述措施有效提高了网络搜索成功率，在一定程度上解决了局部极小值的问题．
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| 关 键 词： | 小波神经网络 粒子群优化算法 平移参数 伸缩参数 |
| 文章编号： | 1671-4512（2006）08-0043-03 |
| 收稿时间： | 2005-05-19 |
| 修稿时间： | 2005年5月19日 |
Initialization of the wavelet parameters and the applications of advanced PSO algorithm in WNN |
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| Cen Yigang,Sun Debao,Li Ning.Initialization of the wavelet parameters and the applications of advanced PSO algorithm in WNN[J].JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE,2006,34(8):43-45. |
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| Authors: | Cen Yigang Sun Debao Li Ning |
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| Abstract: | PSO algorithm to instead of the BP algorithm for the wavelet neural network training was used. The advanced PSO algorithm is proposed by aiming at the local minimum problem. The initialization method of the scale parameters and the translation parameters of the wavelet are proposed in order to avoid the network blind searching. According to these means, the network convergence rate is greatly enhanced and the iteration is decreased. Proven by the simulations of the nonlinear function approximation, the network convergence rate is improved efficiently and the local minimum problem is solved in a certain degree. |
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| Keywords: | wavelet neural network(WNN) particle swarm optimizer(PSO) translation parameter scaling parameters |
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