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 |
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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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