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1.Based on the steepest descent method and the conjugate gradient method, a hybrid algorithm is proposed in this paper, and its global convergence is proved.
摘要将最速下降法与共扼梯度法有机结合起来,构造出一种混合优化算法,并证明其全局收敛性。收藏指正
2.To deal with the defects of the steepest descent in slowly converging and easily immerging in partial minimum frequently, after analyzing the linear hunting method developed by Fletcher and Reeves, the improved conjugate gradient algorithm is brought forward to solve the problem.
针对一般BP算法收敛速度慢,易陷入局部极小值的缺陷,本文受Fletcher-Reeves线性搜索方法的指引,提出了基于改进共轭梯度法的BP算法。收藏指正
3.The hybrid algorithm raises the convergence rate of the conjugate gradient method and solves the problem for which the convergence rate of the steepest descent method get slower when the isopleth of goal function is oblong.
这种混合优化算法结合了共扼梯度法和最速下降法产生搜索方向,既提高了共扼梯度算法的收敛速度,又解决了目标函数的等值线是扁长椭球时,最速下降法下降缓慢的问题,具有收敛速度快、收敛范围大、适应面广等特点。收藏指正
4.In this algorithm, Takagi Sugeno fuzzy neural network is used for the parametric learning of a fuzzy rule base with steepest descent method. Then, the structure of fuzzy rule bases is optimized with the binary coding method, in which a rule corresponds to a gene bit, and a rule base to a chromosome.
利用TakagiSugeno模糊神经网络对一个模糊规则基进行参数学习,学习方法为梯度下降法,然后利用遗传算法对规则基进行结构调整,采用二进制编码方法,一条规则对应于一个基因位,一个规则基对应于一条染色体。收藏指正
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