An on-line fuzzy predictor from real-time data

The algorithm of on-line predictor from input-output data pairs will be proposed. In this paper, it proposed an approach to generate fuzzy rules of predictor from real-time input-output data by means of ARMA model concept for unknown system. It includes two phase: (1). Generating fuzzy rules phase, (2). On-Line Learning phase; If the error between the real output and the predictor's output is larger than the desired error, it means that the lack of the fuzzy rules. Thus, it will generate some new fuzzy rules for the fuzzy predictor or adding an output term in the premise part of fuzzy rules. From the generating fuzzy rules phase, it can on-line generate the fuzzy predictor. In another word, some redundant rules may be generated from bad information after learning. They may be incoming data include outliers, noises or uncertainties. Such bad rules will be discarded by a usage degree constant. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule may be adjusted by on-line learning, when the prediction error into a pre-defined bound. In the simulation example, a nonlinear time-varying process operating in open loop is illustrated. Simulations and real-time results show the advantages of the proposed method.