摘要
构造了一类新的高效分段活化函数，很好地解决了ＢＰ算法学习收敛速度慢的问题，并提出了一种自适应调整网络参数的新算法，从而大大提高了算法的学习效率和综合性能．
In this paper,not only a kind of new activation functions,called segment activation function(SAF),but also the improved back propagation algorithm in which networks parameters can be adaptively adjusted is proposed to solve two key problems:slow convergence and low learning efficiency which exist in the conventional BP ANN and restrict its applications,so the learning efficiency and comprehensive properties are greatly improved.Moreover,the procedure of modeling for the optional selection systems which have been applied to the optimal selection for fine chemical experiment conditions is discussed.The application results are very satisfactory.
出处
《暨南大学学报（自然科学与医学版）》
CAS
CSCD
1998年第1期81-87,共7页
Journal of Jinan University(Natural Science & Medicine Edition)
