| 基于改进粒子群算法的BP神经网络模型研究 |
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| 引用本文： | 姚尔果,闫秋粉,南振岐,薛小虎.基于改进粒子群算法的BP神经网络模型研究[J].佳木斯工学院学报,2012(1):107-109. |
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| 作者姓名： | 姚尔果 闫秋粉 南振岐 薛小虎 |
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| 作者单位： | [1]西北师范大学数学与信息科学学院,甘肃兰州730070 [2]新疆乌鲁木齐市军区测绘信息中心,新疆乌鲁木齐830000 |
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| 摘 要： | 为解决BP神经网络局部性收敛度慢的问题，提出了基于改进粒子群算法的BP神经网络模型．该方法通过粒子群进化速率动态调整惯性权重因子，提高了算法的收敛速度和全局搜索最优值的能力．提出的模型和改进的算法模拟仿真表明：该方法对收敛速度和精度有更好的拟合性．
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| 关 键 词： | 粒子群算法 进化速率 惯性权重因子 BP神经网络 |
BP Neural Network Model Research Based on Improved Particle Swarm Optimization Algorithm |
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| Authors: | YAO Er guo YAN Qiu fen NAN Zhen qi XUE Xiao hu |
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| Affiliation: | College of Mathematics and Information Science, Northwest Normal University, Lanzhou 730070, China ; Military Mapping In. formation Center of Xinjiang, Urumqi 830000, China |
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| Abstract: | To solve local convergence ot slow problems ot Bt＂ neural network, a tit＂ neural network model based on improved particle swarm optimization （PSO） algorithm was proposed. The convergence speed and theability of optimal value global searching were promoted by evolutionary rate adaptive inertia weight factor. Final- ly, the fitness of convergence speed and accuracy was validated by practical application through simulating the model and the improved algorithm. |
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| Keywords: | particle swarm optimization algorithm Evolutionary rate Inertia weight factor BP neuralnetwork |
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