Adaptive fuzzy system based on competitive learnign and its application to fault diagnosis of non-linear system

摘要

The fault diagnosis based on analytical redundance depends on system's model, whicl it is difficult to get the system's model when the system is non-linear or uncertain time-varying, there are some difficulties to put this method into application of non-linear system. The adaptive fuzzy system can build the system's model by learning, and the model's parameter established by this method has clear mean, it provide an effective way to solve the above problem. The key of the application of the adaptive fuzzy system is the determination of the parameters of the adaptive fuzzy system. Different from the other researchers, the authors convert the learning process to cluster and linear optimum, based on competitive learning nad least-square error criterion, suggest a learning algorithm for the adaptive fuzzy system, the experiment in certain servo-mechanism get very good result.