摘要
为了克服BP网络自身算法的缺陷,得到更高的学习精度和更快的收敛速度,将遗传算法、小波分析、人工神经网络和模拟退火思想结合起来,提出了一种遗传小波网络:即用遗传算法来学习小波神经网络层间的权值、尺度参数和位置参数。用两维和三维XOR问题对其性能分别进行了测试,取得了理想的效果;将其应用于矿压预报,得到了比传统神经网络更优的效果。
In order to overcome the algorithm shortcoming of BP network and obtain much higher accuracy and faster speed, a wavelet network based on genetic algorithm was put forward, which combined genetic algorithm with wavelet analysis and artificial neural network and simulated annealing thought, to study various parameters such as weights etc by genetic algorithm. Its property was tested by solving the problem of twodimension XOR and threedimension XOR, and good results of them were obtained. And it was also used to solve the problem of forecast in the field of ground pressure, more optimal results than typical BP network have been obtained.
出处
《中国有色金属学报》
EI
CAS
CSCD
北大核心
1999年第2期448-452,共5页
The Chinese Journal of Nonferrous Metals
基金
中国博士后科学基金
煤炭高校优秀青年基金
关键词
矿山压力
预报
遗传算法
小波神经网络
artificial neural networkwavelet network based on GAground pressureforecast