A self-organizing recurrent fuzzy neural network based on multivariate time series analysis

Ding, Haixu; Li, Wenjing; Qiao, Junfei

doi:10.1007/s00521-020-05276-w

Cited by 20 publications

(3 citation statements)

References 57 publications

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“…Adaptation of the rule base during the neural learning process is therefore another possible direction for future research. Future studies might also enhance the proposed model by replacing the neural intuitionistic fuzzy TSK system in the forecasting stage with recently proposed extensions of recurrent neural fuzzy systems (Tang et al, 2021;Ding et al, 2021) and by extending the model to allow for forecasting interval-valued TS (Maciel et al, 2021). For the latter, a neural interval type-2 intuitionistic fuzzy system is suggested, in which both the fuzzification and defuzzification of the interval-valued TS should be carried out separately for its lower and upper bounds.…”

Section: Discussionmentioning

confidence: 99%

Neural intuitionistic fuzzy system with justified granularity

Hájek

Froelich

Olej

et al. 2022

Neural Comput & Applic

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Fuzzy systems are commonly used to model uncertainties occurring in data. They have been intensively investigated and extended to be used for the construction of time series forecasting models. In particular, intuitionistic fuzzy sets have been used to capture higher levels of uncertainty occurring in the modeled data. Neural networks were also used to reflect non-linearity relationships frequently observed in time series. Following this line of research, in this paper, we propose a new hybrid fuzzy system merging the most promising data representation methods with an advanced optimization technique, which is the principle of justified granularity. In this technique, we construct an innovative time series forecasting model. In the experimental part of the Article Title paper, we demonstrate the advantages arising from applying the proposed approach to metal price forecasting. We provide solid evidence that the proposed model is competitive with other state-of-the-art models known from the literature for forecasting horizons of one and five days.

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Section: Discussionmentioning

confidence: 99%

Neural intuitionistic fuzzy system with justified granularity

Hájek

Froelich

Olej

et al. 2022

Neural Comput & Applic

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“…Moreover, robot polishing skills consist of time-series data, where data at different moments can have an influence on each other. And Recurrent neural network (RNN) can memorize historical information to adapt to more complex dynamic environments [18]. So RNN is more suitable for force sensor feedback model building compared to feedforward neural network (FNN).…”

Section: Introductionmentioning

confidence: 99%

AL-ProMP: Force-relevant skills learning and generalization method for robotic polishing

Wang

Chen

Peng

et al. 2023

Robotics and Computer-Integrated Manufacturing

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“…Later, Agrawal and Pal et al did a lot of excellent work by using PSO algorithm and generalized type-2 fuzzy set in [38,39]. Especially, Khater and Ding et al studied the adaptive online learning and multivariable time series analysis for a class of recurrent fuzzy neural networks in [40,41], respectively. In 2019, Hsieh and Jeng [42] utilized locally weighted polynomial regression to propose a single index fuzzy neural network, and used output an activation function and polynomial function to approximate the constructed network.…”

Section: Introductionmentioning

confidence: 99%

Convergence and gradient algorithm of a class of neural networks based on the polygonal fuzzy numbers representation

Sun

Wang

2022

Complex Intell. Syst.

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As a special case of general fuzzy numbers, the polygonal fuzzy number can describe a fuzzy object by means of an ordered representation of finite real numbers. Different from general fuzzy numbers, the polygonal fuzzy numbers overcome the shortcoming of complex operations based on Zadeh’s traditional expansion principle, and can maintain the closeness of arithmetic operation. Hence, it is feasible to use a polygonal fuzzy number to approximate a general fuzzy number. First, an extension theorem of continuous functions on a real compact set is given according to open set construction theorem. Then using Weierstrass approximation theorem and ordered representation of the polygonal fuzzy numbers, the convergence of a single hidden layer feedforward polygonal fuzzy neural network is proved. Secondly, the gradient vector of the approximation error function and the optimization parameter vector of the network are given by using the ordered representation of polygonal fuzzy numbers, and then the gradient descent algorithm is used to train the optimal parameters of the polygonal fuzzy neural network iteratively. Finally, two simulation examples are given to verify the approximation ability of the network. Simulation result shows that the proposed network and the gradient descent algorithm are effective, and the single hidden layer feedforward network have good abilities in learning and generalization.

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A self-organizing recurrent fuzzy neural network based on multivariate time series analysis

Cited by 20 publications

References 57 publications

Neural intuitionistic fuzzy system with justified granularity

Neural intuitionistic fuzzy system with justified granularity

AL-ProMP: Force-relevant skills learning and generalization method for robotic polishing

Convergence and gradient algorithm of a class of neural networks based on the polygonal fuzzy numbers representation

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