NDLI: Robust Pareto design of ANFIS networks for nonlinear systems with probabilistic uncertainties

Content Provider	IEEE Xplore Digital Library
Author	Jamali, A. Nariman-Zadeh, N. Ashraf, H. Jamali, Z.
Copyright Year	2011
Description	Author affiliation: Department of Mechanical, engineering, Faculty of Engineering, The University of Guilan, Rasht, Iran (Nariman-Zadeh, N.; Ashraf, H.) \|\| Department of Mechanical engineering, Faculty of Engineering, The University of Guilan, Rasht, Iran (Jamali, A.) \|\| Department of Civil engineering, Faculty of Engineering, The University of Guilan, Rasht, Iran (Jamali, Z.)
Abstract	In this paper, multi-objective evolutionary Pareto optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling of nonlinear systems using input-output data sets with probabilistic uncertainties. In this way, A Monte Carlo Simulation (MCS) is first performed to generate input-output data set using some probabilistic distributions. Multi-objective uniform-diversity genetic algorithms (MUGA) are then used for Pareto optimization of ANFIS networks. The important conflicting objectives of ANFIS networks that are considered in this work are, namely, the mean and variance of both Training Error (TE) and Prediction Error (PE) of such ANFIS models. It is shown that a robust ANFIS can be simply obtained using a criterion based on four values of means and variances of both TE and PE. The probabilistic evolved ANFIS model exhibits much more robustness to the uncertainties involved within the input-output data sets than that of the deterministic evolved ANFIS model. It is shown that ANFIS can be successfully applied for input-output data set with uncertainties so that a robust model can be compromisingly obtained from some non-dominated optimum ANFIS models
Starting Page	300
Ending Page	304
File Size	341276
Page Count	5
File Format	PDF
ISBN	9781612849195
e-ISBN	9781612849225
DOI	10.1109/INISTA.2011.5946080
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-15
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pareto Adaptation models Uncertainty Monte Carlo Robust model Predictive models Probabilistic logic Data models Robustness ANFIS Genetic algorithms
Content Type	Text
Resource Type	Article

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