基于操作条件反射自动机的仿生自主学习方法

摘要

This paper presents a bionic autonomous learning method based on operant conditioning automata, and designs a cognitive model of Skinner operant conditioning automata. The model adds a unit of behavior propensity to generate learning mechanism of ＇ reaction-strengthening＇ , and simulates nature biotic randomly compliance. A recursive learning algorithm is proposed, which is to simulate biotic operant conditioning mechanism and let it have bionic self-organization function including self-learning function and self-adaptive function. Finally, the model is proved to have the bionic self-learning ability and can be used to descript, simulate, and design various self-organizing systems.%提出了基于操作条件反射的仿生自主学习方法，设计了操作条件反射自动机（OCM）的认知模型．与原来的学习自动机相比，该模型增加了状态取向单元，利用“反应一强化”的学习机理，来模拟自然界生物的“随机应变性”．设计了OCM的递归学习算法，用于模拟生物的操作条件反射机制，使其具有仿生的自组织功能，包括自学习和自适应功能．通过模拟Skinner鸽子实验和倒立摆平衡控制实验，验证了该模型具有一定的仿生自主学习能力，可用于描述、模拟和设计各种自组织系统．