【摘要】 本文讨论了目标识别的协同方法在不平衡注意参数条件下的动力学行为 ,并提出了不平衡注意参数条件下的遗传协同学习算法 (GSLA) .该算法利用遗传算法的全局最优搜索能力 ,对协同神经网络的注意参数进行全局优化 .对从“车牌识别系统”中得到的数字样本的实验证明 :新算法能有效地在注意参数空间搜索全局最优解 ,挖掘出协同方法在目标识别方面的最大潜能 .另外 ,本文还将新算法与利用奖惩学习算法的协同学习算法进行了全局优化能力的比较 ,发现新算法具有收敛快和全局最优搜索能力强的特点更多还原

【Abstract】 An analysis of dynamics of synergetic approach on target recognition under unbalanced attention parameters is presented and a genetic synergetic learning algorithm (GSLA) for synergetic neural network (SNN) is proposed in this paper.The GSLA is demonstrated to have strong ability of searching globally optimal solutions in the space of attention parameters,and makes the SNN do its best on target recognition.In addition,A comparison between the GSLA and the synergetic learning algorithm based on award penalty learning mechanism is made,and shows that the GSLA can converge more quickly and has stronger power of globally optimal searching than the algorithm based on award penalty learning mechanism.更多还原