A New Recurrent Neurofuzzy Network for Identification of Dynamic Systems

Abstract

AbstractIn this paper a new structure of a recurrent neurofuzzy network is proposed. The network considers two cascade-interconnected Fuzzy Inference Systems (FISs), one recurrent and one static, that model the behaviour of a unknown dynamic system from input-output data. Each FIS’s rule involves a linear system in a controllable canonical form. The training for the recurrent FIS is made by a gradient-based Real-Time Recurrent Learning Algorithm (RTRLA), while the training for the static FIS is based on a simple gradient method. The initial parameter conditions previous to training are obtained by extracting information from a static FISs trained with delayed input-output signals. To demonstrate its effectiveness, the identification of two non-linear dynamic systems is included.KeywordsFuzzy SystemFuzzy Inference SystemRecurrent Neural NetworkLyapunov StabilityInitial Parameter ConditionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.