Abstract:

Platform on probabilistic automata and combined with evolution thought of genetic algorithm, thispaper constructs a bionic learning model which can reflect the essence of Skinner operant conditioning. The designed learning model is named as genetic algorithm-operant conditioning probabilistic automaton (GA-OCPA) bionic autonomous learning system. After each learning trial, the learning system firstly obtains the information entropy value based on operant conditioning (OC) learning result and uses it as the fitness of individual. And then genetic algorithm is performed based on information entropy value to find the optimal individual. At last, the OC learning algorithm is performed to learn the optimal operant action in optimal individual, and correspondingly a new information entropy value will be obtained. The convergence theorems for the learning algorithm of GA-OCPA bionic learning system is presented, and the simulation analyses in motion balancing control of two-wheeled robot demonstrate that the learning of GA-OCPA bionic learning system is a process of autonomously acquiring and epurating knowledge and has high adaptive ability.