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A METHOD FOR TRAINING A RADIAL BASIS FUNCTION NETWORK
A METHOD FOR TRAINING A RADIAL BASIS FUNCTION NETWORK
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机译:一种训练径向基函数网络的方法
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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
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