| 摘要: |
| 摘要：提出了一种利用蚁群算法训练神经网络的算法，进行混沌系统辨识，并与神经网络、遗传神
经网络对同一混沌系统辨识的结果进行比较；实验表明：利用蚁群算法训练神经网络进行混沌系统
的辨识，能克服BP求解精度低、搜索速度慢、易于陷入局部极小的缺点；与遗传神经网络相比，虽
然执行时间唷所增加，但求解精度显著提高，可有效用于混沌系统辨识。 |
| 关键词: 关键词：神经网络 混沌系统 蚁群算法 系统辨识 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Training BP neural networks with ACO for identification of chaotic systems |
|
CHENG W ei
|
| Abstract: |
| Abstract：BP is the most commonly used artificial neural networks，but it sufers from extensive computations，rel—
atively slow convergence speed and other possible weaknesses for complex problems．Genetic Algorithm (GA)has
been successfully used to train neural networks，but often with the result of exponential computational complexities
and hard implementation．Hence Ant Colony Optimization(ACO)is used to train BP in the paper．The eficiency
of BP trained with ACO is compared with those of BP and．_BP trained with GA based on the identification of the
same chaotic system．Comparison based on the searching precision and convergence speed of each method show that
BP trained with ACO is dominant and effective to identify chaotic system． |
| Key words: Key words：neural networks chaotic system ant colony optimization system identificatio |
