| 基于粒子群优化算法的BP神经网络在图像识别中的应用 |
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| 引用本文： | 高艳霞,李禹生. 基于粒子群优化算法的BP神经网络在图像识别中的应用[J]. 武汉工业学院学报, 2006, 25(4): 35-38 |
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| 作者姓名： | 高艳霞 李禹生 |
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| 作者单位： | 武汉工业学院,计算机与信息工程系,湖北,武汉,430023 |
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| 摘 要： | 介绍了一种采用微粒群算法与BP算法相结合的方法用于BP神经网络模型优化，来提高模型的收敛速度和精度。仿真结果表明，与BP算法相比较，PSO—BP学习算法训练的神经网络不仅训练时间明显缩短，而且其预报精度也得到了较大的提高。
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| 关 键 词： | 粒子群优化算法 人工神经网络 图像识别 |
| 文章编号： | 1009-4881（2006）04-0035-04 |
| 收稿时间： | 2006-06-22 |
| 修稿时间： | 2006-06-22 |
IMAGE RECOGNITION BY BP NEURAL NETWORKS MODEL BASED ON PARTICLE SWARM OPTIMIZATION |
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| GAO Yan-xia,LI Yu-sheng. IMAGE RECOGNITION BY BP NEURAL NETWORKS MODEL BASED ON PARTICLE SWARM OPTIMIZATION[J]. Journal of Wuhan Polytechnic University, 2006, 25(4): 35-38 |
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| Authors: | GAO Yan-xia LI Yu-sheng |
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| Affiliation: | Department of Computer and Information Engineering, Wuhan Polytechnic University, Wuhan 430023, China |
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| Abstract: | A new algorithms which combines Particle Swarm Optimizer(PSO) algorithm with BP(Back-Propagation) algorithm is introduced in this paper and applied to BP neural networks to optimize the parameters of BP neural networks so as to improve the convergence speed and precision of BP neural networks.Compared with the BP algorithm,the simulation results show that PSO-BP algorithm exhibits shorter training rate and better predicting accuracy than the BP algorithm. |
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| Keywords: | particle swarm optimization algorithm artificial neural network image recognition |
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