摘要
提出了一种改进的自适应遗传算法 Ｉ Ａ Ｇ Ａ，它利用网络结构的特点，采用前向自适应技术，实现对神经网络的有效训练．实验表明，该算法优于 Ｂ Ｐ算法、标准遗传算法 Ｂ Ｇ Ａ 和普通自适应遗传算法 Ａ Ｇ Ａ。
Layered feedforward neural network training algorithm based on traditional BP algorithm may lead to entrapment in local optimum, and has the defects such as slow convergent speed and unsatisfied dynamic character which reduce the study ability of the network. This paper presents an improved adaptive genetic algorithm (IAGA) for training the neural network efficiently that uses a forward adaptive technique and takes the advantages of the network architecture. The experimental results show that our algorithm outperforms BP algorithm, BGA algorithm and AGA algorithm, and the dynamic character, training accuracy and efficiency proved greatly.
出处
《武汉大学学报（自然科学版）》
CSCD
1999年第3期363-366,共4页
Journal of Wuhan University(Natural Science Edition)
基金
国家自然科学基金
