基于小脑-基底神经节机理的行为认知计算模型  

Behavior Cognition Computational ModelBased on Cerebellum and Basal Ganglia Mechanism

作　　者：陈静[1] 阮晓钢[1] 戴丽珍[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124

出　　处：《模式识别与人工智能》2012年第1期29-36,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60774077;61075110);国家863计划项目(No.2007AA04Z226);北京市自然科学基金项目(No.4102011);北京市教委重点项目(No.KZ200810005002)资助

摘　　要：针对智能体的行为认知问题,提出一种小脑与基底神经节相互协调的行为认知计算模型.该模型核心为操作条件学习算法,包括评价机制、行为选择机制、取向机制及小脑与基底神经节的协调机制.初期的学习信号来自于下橄榄体和黑质两部分,在熵的意义上说明该算法是收敛的.采用该学习方法为自平衡两轮机器人建立运动神经认知系统,利用RBF网络逼近行为和评价网络.仿真实验表明该方法改善仅有基底神经节作用的行为-评价算法学习速度慢和失败次数多的问题,学习后期通过温度的不断降低,加快学习速度,震荡逐渐消失,改善学习效果.Aiming at agent＇s behavioral cognition problem,a behavior cognition computational model based on the coordination of cerebellum and basal ganglia is proposed.Operant conditioning learning algorithm is the central algorithm including evaluation mechanism,action selection mechanism,tropism mechanism,and the coordination mechanism between cerebellum and basal ganglia.The learning signals come from not only the Inferior Olive but also the Substantia Nigra in the beginning.The convergence of the algorithm can be guaranteed in the sense of entropy.With the proposed method,a motor nerve cognitive system for the self-balancing two-wheeled robot has been built using the RBF neural network as the actor and evaluation function approximator.The simulation results show that the learning speed is increased as well as the failure times are reduced by the proposed method than by the Actor-Critic method with the only Basal Ganglia mechanism.Through decreasing temperature in the late stage,the learning speed is increased and the vibration disappeares eventually,and the learning effect is improved.

关 键 词：小脑 基底神经节 操作条件反射 自平衡两轮机器人 行为认知 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]