Implementing adaptive fuzzy logic controllers with neural networks.
PublisherThe University of Arizona.
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AbstractThe goal of intelligent control is to achieve control objectives for complex systems where it is impossible or infeasible to develop a mathematical system model but expert skills and heuristic knowledge from human experiences are available for control purposes. To this end, an intelligent control system must have the essential characteristics of human control experiences, i.e., linguistic knowledge representation, which facilitates the process of knowledge acquisition and transfer, and adaptive knowledge evolution or learning, which leads to the improvement in system performance and knowledge. This dissertation presents an efficient approach that combines fuzzy logic and neural networks to capture these two important features required for an intelligent control system. A design method for adaptive neuro-fuzzy controllers has been proposed using structured neuro-fuzzy networks. The structured neuro-fuzzy networks consist of three types of subnets for pattern recognition, fuzzy reasoning, and control synthesis, respectively. Each subnet is constructed directly from the decision-making procedure of fuzzy logic based control systems. In this way, a one-to-one mapping between a fuzzy logic based control system and a structured neuro-fuzzy network is established. This mapping enables us to create a knowledge structure within neural networks based on fuzzy logic, and to give a learning ability to fuzzy controls using neural networks. From the perspective of neural networks, the proposed design method offers a mechanism to: construct networks with heuristic knowledge, instead of using digital training pairs, which are much more difficult to get, build decision structures into networks, which divide a network into several functional regions and make the network no longer just as a black-box function approximator, and conduct network learning in a distributed fashion, i.e., each sub-network of different functional regions can learn its own function independently. On the other hand, from the perspective of fuzzy logic, the proposed design method provides a tool to: refine membership functions, inference procedures, and defuzzification algorithms of fuzzy control systems; generate new fuzzy control rules so that fuzzy control systems can adapt to gradual changes in environments and implement parallel execution of rule matching, firing, and defuzzification. Several simulation studies have been conducted to demonstrate the use of the structured neuro-fuzzy networks. The effectiveness of the proposed design method has been clearly shown by the results of these studies. These results have also indicated that fuzzy logic and neural networks are complementary and their combination is ideal to achieve the goal of intelligent control.
Degree ProgramSystems and Industrial Engineering