Research On Fuzzy Neural Control Based On Genetic Algorithms

Genetic Algorithms, Neural Network and Fuzzy Control are important research fields in artificial intelligence. The research on combinations of genetic algorithms, neural network and fuzzy logic is attracting the attention of many researchers because of many common and complementary features among them. The purpose of this thesis is to discuss the ways to combine genetic algorithms, neural network and fuzzy logic control and explore how to acquire the channel of intelligent information processing. Moreover, the application of the fuzzy neural network based on genetic algorithms in the field of industry boiler combusting system is discussed.The clue of this paper is genetic algorithms fuzzy neural network fuzzy neural network based on genetic algorithms. The whole paper concludes three parts: genetic algorithms, fuzzy neural network and fuzzy neural network based on genetic algorithms. The content of this thesis is as follows:1. The basic theory of genetic algorithmsFirstly, through comparing the familiar researching methods and characteristics, the characteristics of GA and the necessary of researching GA are discussed. Then, the generation & progress, the prime researching fruits, the important researching tasks in future in the field of GA are discussed. The significant content in this part lies in the basic principle, flow and the realization in MATLAB of GA. Finally, as the example of industry boiler combusting system; the application of GA is discussed. Through the researching of the industry boiler combusting thermal efficiency self-optimized system , we can draw a conclusion: as a kind of self-optimized method, generic algorithms has the virtues of the simple principle, the easy realization, prompt self-optimized paces and robustness.2. The fuzzy neural control theory combining neural network and fuzzy controlFor the fuzzy neural control theory, the clue of this part is the neuron in anatomy-the neuron model in the field of control neural network fuzzy neural network. First, the structure, function, basic attribution and learning rules of neural network are discussed. Next, the topological structure, classification, the characteristic of membership function and the definition methods of excitation function in the field of neural network are discussed. Finally, as the example of Fuzzy Cerebella Model Articulation Controller, the application of neural network is discussed. The result of simulink indicated that comparing with Cerebella Model Articulation Controller, the FCAMC have the virtues of higher precision and the faster-paces. As a result, it has the more widely researching value. 3. The fuzzy neural control theory based on genetic algorithms The main ideas of this part lie in the control strategy that combine GA & fuzzy control and combine GA & neural network. Firstly, genetic algorithms are applied into fuzzy control. It lies in the fuzzy genetic algorithms and fuzzy optimizing using GA. Then, genetic algorithms are combined into neural network. It lies in the three aspects as follows: network learning, network designation and network analysis. Finally, the control strategy, which combines GA and fuzzy neural network, is discussed. In addition, as an example of the boiler combusting system, the application of fuzzy neural control based on genetic algorithms is discussed. The result of simulink indicated that it has the virtue of the faster-paces and robustness.The control theory of combining genetic algorithms into fuzzy control and neural network has the remarkable virtue. It indicates the immense application potential in the field of intelligent control. As a result, it is accelerating the research in the field of artificial intelligent that is a significant research direction in future.