| 神经网络基于改进型粒子群算法的研究 |
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| 引用本文： | 郭伟,陈广义.神经网络基于改进型粒子群算法的研究[J].佛山科学技术学院学报(自然科学版),2007,25(5):21-24. |
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| 作者姓名： | 郭伟 陈广义 |
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| 作者单位： | 1. 广东工业大学,自动化学院,广东,广州,510096;佛山科学技术学院,自动化系,广东,佛山,528000
2. 佛山科学技术学院,自动化系,广东,佛山,528000 |
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| 摘 要： | 提出一种基于被动聚集型粒子群优化算法来提高人工神经网络的性能,被动聚集是保持群体完整性的生物力量,该算法引入了生物学中被动聚集的概念,使得信息在种群各粒子间传播,保持种群的多样性。仿真实验表明:基于改进型粒子群优化算法的神经网络可以有效降低训练次数和均方误差。
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| 关 键 词： | 神经网络 粒子群 被动聚集 |
| 文章编号： | 1008-0171（2007）05-0021-04 |
| 修稿时间： | 2007-04-23 |
Research on an improved PSO-based ANN |
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| GUO Wei,CHEN Guang-yi.Research on an improved PSO-based ANN[J].Journal of Foshan University(Natural Science Edition),2007,25(5):21-24. |
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| Authors: | GUO Wei CHEN Guang-yi |
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| Institution: | 1. Faculty of Automation, Guangdong University of Technology, Guangzhou 510096, China; 2. Department of Automation Engineering, Foshan University, Foshan 528000, China |
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| Abstract: | This paper presents a particle swarm optimizer(PSO) with passive congregation to improve the performance of artificial neural networks(ANN).Passive congregation is an important biological force preserving swarm integrity.By introducing passive congregation to PSO,information can be transferred among individuals of the swarm.Experimental results indicate that the ANN based on improved PSO can reduce the times of training and MSE effectively. |
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| Keywords: | artificial neural networks particle swarm optimizer passive cogregation |
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