| 氧乐果合成过程的PSO-回归BP网络建模方法 |
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| 引用本文： | 冯冬青,杨书显. 氧乐果合成过程的PSO-回归BP网络建模方法[J]. 郑州大学学报(理学版), 2011, 0(3) |
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| 作者姓名： | 冯冬青 杨书显 |
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| 作者单位： | 郑州大学电气工程学院; |
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| 基金项目： | 国家自然科学基金资助项目,编号60774059 |
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| 摘 要： | 为了提高模型效率,更好地反映实际系统的动态特性,根据氧乐果合成过程特点确定了PSO-回归BP网络结构.采用惯性权重动态调整的粒子群算法进行初始寻优,并基于改进的BP算法对优化的网络权阈值进一步精确优化,建立了氧乐果合成过程的PSO-回归BP网络模型.仿真结果表明,所建模型误差小、收敛速度快、网络泛化能力强,能更好地反映实际对象特点.
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| 关 键 词： | 粒子群算法 回归BP网络 氧乐果合成 温度对象 |
Modeling Method of PSO-recurrent BP Network for Omethoate Synthesis Process |
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| FENG Dong-qing,YANG Shu-xian. Modeling Method of PSO-recurrent BP Network for Omethoate Synthesis Process[J]. Journal of Zhengzhou University(Natrual Science Edition), 2011, 0(3) |
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| Authors: | FENG Dong-qing YANG Shu-xian |
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| Affiliation: | FENG Dong-qing,YANG Shu-xian(School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China) |
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| Abstract: | In order to improve the model efficiency and show dynamic characteristic of the system,the modeling method of PSO-recurrent BP network for omethoate synthesis process was studied.Firstly,the structure of PSO-recurrent BP network was determined according to the features of the object.Secondly,PSO algorithm was used to optimize the weight and threshold of BP neural network.Finally,the improved BP algorithm was used to train the pre-optimized weight and threshold for getting further accurate parameters of the ... |
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| Keywords: | PSO algorithm recurrent BP neural network omethoate synthesis temperature object |
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