| 基于混合遗传神经网络的百米跑成绩预测方法 |
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| 引用本文： | 陈海英,郭巧,徐力.基于混合遗传神经网络的百米跑成绩预测方法[J].计算机仿真,2004,21(2):89-91. |
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| 作者姓名： | 陈海英 郭巧 徐力 |
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| 作者单位： | 北京理工大学机器人研究中心,北京,100081 |
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| 基金项目： | 国家自然科学基金资助项目(60171018) |
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| 摘 要： | 在遗传算法(Genetic ALgorithm)与BP(Back Propagation)网络结构模型相结合的基础上，设计了用遗传算法训练神经网络权重的新方法，并把这种方法用于运动员百米跑成绩预测。与BP算法和LM(Levenberg Marquardt)算法相比，基于混合遗传算法的神经网络不仅有较快的学习速度和较好的学习精度，而且网络的泛化能力(Generalization Ability)得到了很大提高。
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| 关 键 词： | 百米跑 成绩预测 混合遗传神经网络 遗传算法 人体运动能力 权值优化 |
| 文章编号： | 1006-9348(2004)02-0089-03 |
| 修稿时间： | 2003年4月18日 |
Prediction Method of 100Sprint Performance Based on Hybrid Genetic Neural Network |
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| CHEN Hai-ying,GUO Qiao,XU Li.Prediction Method of 100Sprint Performance Based on Hybrid Genetic Neural Network[J].Computer Simulation,2004,21(2):89-91. |
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| Authors: | CHEN Hai-ying GUO Qiao XU Li |
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| Abstract: | Based upon the combination of Genetic Algorithm and BP Neural Network, this paper puts forward a new approach to learn the weights of BP using GA, and applies this method firstly to predict 100m sprint performance of athletes. Compared with BP algorithm and LM algorithm, the neural network based on hybrid GA not only has quick convergence speed and better learning precision, but also the generalization ability of network has improved greatly. |
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| Keywords: | 100m sprint Genetic algorithm Neural network Weightsoptimizing |
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