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信息与控制
针对刚性机械臂系统的控制问题，提出基于极限学习机 ELM 的自适应神经控制算法 极限学习机随机选择单隐层前馈神经网络 SLFN 的隐层节点及其参数，仅调整其网络的输出权值，以极快的学习速度获得良好的推广性 采用李亚普诺夫综合法，使所提出的ELM控制器通过输出权值的自适应调整能够逼近系统的模型不确定性部分，从而保证整个闭环控制系统的稳定性 将该自适应神经控制器应用于2自由度平面机械臂控制中，并与现有的径向基函数 RBF 神经网络自适应控制算法进行比较 实验结果表明，在同等条件下，ELM控制器具有良好的跟踪控制性能，表明了所提出控制算法的有效性. 2015,Vol.44(3) 257-262 摘要. 957 KB ( 55. 王云甘, 王忠锋, 于海斌, 李力刚, 黄剑龙. 2015,Vol.44(3) 263-269 摘要. 1348 KB ( 18. 徐迎, 曾水玲, 伍文源. 2015,Vol.44(3) 270-275 摘要. 1003 KB ( 12. 柏猛, 李敏花, 吕英俊. 2015,Vol.44(3) 276-283,290 摘要. 1979 KB ( 9. 路洁, 李宏光, 宿翀.
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