Modeling And Application Of Neural Networks Based On Game Process

Artificial neural network system is one of main branches of intelligent control technology,which has great application in identification modeling of the nonlinear system,non-linear process prediction,neural networks control and fault diagnosis and so on.It gradually implements its computation process through learning.In the learning process,the joint strength between neurons changes slowly according to the environmental information,gradually storing the environmental information in the neural network,which forms a long-term memory.The learning process wants to form a self-organization system with certain structure obtaining knowledge from external environment by correlation.Game theory is an interdisciplinary approach to the study of human behavior.Most of human learning in social context has an interactive nature: what an individual learns is affected by what other individuals are learning at the same time.Games describe a widely accepted framework for representing interactive decision-making as well as its equilibrium when subjects' behavior interactively affects each other directly.As Artificial neural networks and game theory are both methods of exploring basic rules of human discovering and grasping knowledge,with consideration of their combination,to establish an effective mathematical model is bound to promote the process of grasping and discovering scientific knowledge by using simulation technologies.In an increasingly complex,crowded and competitive world,these factors are extremely significant for a nation to stand among worldly nationalities.After introducing fundamental principles of artificial neural networks and game theory,combining game paradigm with the artificial neural networks,this paper explored the potential value of the neural networks in simulating and predicting human learning process in repeated games,and established a neural network learning model based on game process.In the neural network evolution process,imitating the learning method of human review and contrast,with 'regret' drawn into feedback process,neural networks connection weights were updated.In MATLAB context,the designed learning model was applied in experimental repeated games and the system simulation research and analysis was conducted after training.With different criterions considered,the results showed that this model obviously outperformed other models like reinforcement learning model and the traditional NNET analog perceptron.Finally the game-processed neural networks were further combined with the fuzzy logic,and a fuzzy neural network model based on game process was established,by introducing fuzzy logic into the model structure in which max and min rules in fuzzy logic are made instead of original computation.The simulation results demonstrated that this model is faster and better than the former model.