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A METHOD FOR TRAINING A RADIAL BASIS FUNCTION NETWORK

机译:一种训练径向基函数网络的方法

摘要

A METHOD OF TRAINING THE RBF NETWORK IS PROVIDED, COMPRISING THE STEPS OF FIRSTLY GENERATING AN INITIAL POPULATION (102) AND VELOCITY FOR EVERY PARTICLE IN A POPULATION FOLLOWED BY ASSIGNING ELEMENTS (104) OF A WEIGHT MATRIX AND ELEMENTS OF BINARY VECTOR WITH PARTICULAR VALUES. A LOCAL SEARCH IS THEN APPLIED (106) WHEREBY EVERY PARTICLE IN THE POPULATION UPDATES A PLURALITY OF CENTERS, WIDTHS AND WEIGHTS OF THE RBF NETWORK AND THE POPULATION IS EVALUATED (108) BY USING A PLURALITY OF OBJECTIVE FUNCTIONS SUCH THAT THE OBJECTIVE FUNCTIONS OPTIMIZE THE ACCURACY AND DEGREE OF COMPLEXITY OF THE NETWORK BY MAXIMIZING THE NETWORK CAPACITY AND MINIMIZING THE NETWORK COMPLEXITY. SUBSEQUENTLY, A PERSONAL BEST (PBEST) AND GLOBAL BEST (GBEST) FOR EACH PARTICLE IN THE POPULATION IS INITIALIZED (110) AND A PLURALITY OF NON-DOMINATED VECTORS FOUND IN THE POPULATION ARE STORED (112) INTO TWO ARCHIVES WHICH STORE REAL NON-DOMINATED SOLUTIONS AND BINARY NON-DOMINATED SOLUTIONS RESPECTIVELY. A LAST STEP INVOLVES EXECUTING OPTIMIZATION STEPS (114) REPEATEDLY UNTIL A SET OF TERMINATION CONDITIONS AS DETERMINED BY THE USER IS MET. THE MOST ILLUSTRATIVE DRAWING: FIG. 1
机译:提供了一种训练RBF网络的方法,该方法包括首先生成初始人口(102)和人口中每个粒子的速度的步骤,然后为权重矩阵的元素(104)和带有二值向量的二元向量的元素分配。然后应用局部搜索(106),每个人口参与的成员都会更新RBF网络的中心,宽度和权重,并通过使用目标函数的多个来评估(108)人口通过最大程度地提高网络容量并最小化网络复杂度来提高网络的准确性和精确度。随后,初始化种群的每个个体的个人最佳(最佳)和全球最佳(最佳)(110),并将在该种群中找到的多个非限定向量存储(112)成两个存储真实变量的档案有域解决方案和二进制无域解决方案。最后一步涉及执行优化步骤(114),直到用户确定的一组终止条件一致为止。最具说明性的图纸: 1个

著录项

  • 公开/公告号MY155886A

    专利类型

  • 公开/公告日2015-12-15

    原文格式PDF

  • 申请/专利权人 UNIV MALAYSIA TECH;

    申请/专利号MY2013PI00044

  • 申请日2013-01-09

  • 分类号G06N3/08;G06F15/18;

  • 国家 MY

  • 入库时间 2022-08-21 14:22:58

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