A new Neuro-Fuzzy Inference System with Dynamic Neurons (NFIS-DN) for system identification and time series forecasting

Samanta, S and Suresh, S and Senthilnath, J and Sundararajan, N (2019) A new Neuro-Fuzzy Inference System with Dynamic Neurons (NFIS-DN) for system identification and time series forecasting. In: APPLIED SOFT COMPUTING, 82 .

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Abstract

A new Neuro-Fuzzy Inference System with Dynamic Neurons or NFIS-DN is presented here for discrete time dynamic system identification and time series forecasting problems. The proposed dynamic system based neuron, referred to as Dynamic Neuron (DN) is realized by a discrete-time nonlinear state-space model. The DN is designed such way, that the output considers only the effect of finite past instances, enabling the system with finite memory. The NFIS-DN model has five layers, and DNs are employed only in the layers handling crisp values. The antecedent and the consequent parameters of NFIS-DN are updated using a self-regulated backpropagation through time learning algorithm. The performance evaluation of NFIS-DN has been carried-out using benchmark problems in the areas of nonlinear system identification and time series forecasting. The results are compared with the state-of-the-art method on the neural fuzzy networks. The obtained results clearly suggest that the NFIS-DN performs significantly better while using a smaller or similar number of fuzzy rules. Finally the practical application of the NFIS-DN has been demonstrated using two real-world problems.

Item Type:	Journal Article
Publication:	APPLIED SOFT COMPUTING
Publisher:	ELSEVIER
Additional Information:	copyright for this article belongs to ELSEVIER
Keywords:	Dynamic neuron; Recurrent neural network; Fuzzy system; Self-regulated learning; Backpropagation
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	18 Oct 2019 09:30
Last Modified:	18 Oct 2019 09:30
URI:	http://eprints.iisc.ac.in/id/eprint/63689

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