Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Reduction of fuzzy rule base via singular value decomposition
IEEE Transactions on Fuzzy Systems
On multistage fuzzy neural network modeling
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
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This paper explores a new approach for the modelling and identification of non-linear dynamic systems. A model, named the Decomposed Neuro-Fuzzy Auto-Regressive with eXogenous input model (DNFARX), based on decomposed structure of the fuzzy inference system, is proposed. An evolution of a neural network learning algorithm for the decomposed structure of the fuzzy inference system is suggested. A comparative study of the dynamic system modelling with conventional fuzzy inference system based models and the proposed model is presented for Box-Jenkins data set.