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

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...

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Veröffentlicht in:Applied soft computing Jg. 82; S. 105567
Hauptverfasser: Samanta, S., Suresh, S., Senthilnath, J., Sundararajan, N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2019
Schlagworte:
Backpropagation
Dynamic neuron
Fuzzy system
Recurrent neural network
Self-regulated learning
Backpropagation
Self-regulated learning
Fuzzy system
Recurrent neural network
Dynamic neuron
ISSN:1568-4946, 1872-9681
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Zusammenfassung: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. •The proposed Dynamic Neurons (DN) captures the effect of only finite past instances.•The dynamic Neurons make NFIS-DN capable of tracking rapidly changing system dynamics.•The self-regulated backpropagation through time algorithm is more effective and efficient.•NFIS-DN achieves better accuracy while using a smaller network than other methods.•Results clearly indicate a statistically better performance (Friedman’s test and Bonferroni–Dunn test).
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2019.105567