| 基于粒子群优化算法的BP网络学习研究 |
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| 引用本文： | 潘昊,侯清兰.基于粒子群优化算法的BP网络学习研究[J].计算机工程与应用,2006,42(16):41-43,66. |
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| 作者姓名： | 潘昊 侯清兰 |
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| 作者单位： | 武汉理工大学计算机科学与技术学院,武汉,430070 |
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| 摘 要： | 文章提出了基于粒子群优化的BP网络学习算法。在该算法中,用粒子群优化算法替代了传统BP算法中的梯度下降法,使得改进后的算法具有不易陷入局部极小、泛化性能好等特点。并将该算法应用在了高速公路动态称重系统的设计中,实验证明:这种算法能够明显减少迭代次数、提高收敛精度,其泛化性能也优于传统BP算法。
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| 关 键 词： | BP网络 粒子群优化算法 泛化 |
| 文章编号： | 1002-8331-（2006）16-0041-03 |
| 收稿时间： | 2005-09 |
| 修稿时间： | 2005-09 |
A BP Neural Networks Learning Algorithm Research Based on Particle Swarm Optimizer |
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| Pan Hao,Hou Qinglan.A BP Neural Networks Learning Algorithm Research Based on Particle Swarm Optimizer[J].Computer Engineering and Applications,2006,42(16):41-43,66. |
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| Authors: | Pan Hao Hou Qinglan |
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| Affiliation: | School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070 |
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| Abstract: | A BP neural networks learning algorithm based on Particle Swarm Optimizer(PSO) is proposed in this paper.Among this algorithm,PSO algorithm has substituted the gradient descent method in traditional BP algorithm,this new algorithm does not easily trapped local minima and has better generalization.The algorithm is applied to neural network's training in dynamic weighing system.The results show:this algorithm can reduce number of training and error obviously,it has better generalization than traditional BP algorithm. |
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| Keywords: | BP neural networks PSO algorithm generalization |
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