A hybrid learning algorithm for Interval Type-2 Fuzzy Neural Networks in time series prediction for the case of air pollution

2008 ◽  
Author(s):  
J. R. Castro ◽  
O. Castillo ◽  
P. Melin ◽  
A. Rodriguez-Diaz
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Time Series ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Hybrid Learning ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type

Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction

2013 ◽  
Vol 18 (6) ◽  
pp. 1213-1224 ◽  
Author(s):  
Oscar Castillo ◽  
Juan R. Castro ◽  
Patricia Melin ◽  
Antonio Rodriguez-Diaz
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Prediction ◽  
Fuzzy Neural Networks ◽  
Non Linear ◽  
Fuzzy Neural ◽  
Interval Type

A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

2009 ◽  
Vol 179 (13) ◽  
pp. 2175-2193 ◽  
Author(s):  
Juan R. Castro ◽  
Oscar Castillo ◽  
Patricia Melin ◽  
Antonio Rodríguez-Díaz
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Fuzzy Neural Networks ◽  
Hybrid Learning Algorithm ◽  
Fuzzy Neural ◽  
Interval Type

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

scholarly journals PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights

Axioms ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 14 ◽  
Author(s):  
Fernando Gaxiola ◽  
Patricia Melin ◽  
Fevrier Valdez ◽  
Juan Castro ◽  
Alain Manzo-Martínez
Keyword(s):  
Neural Networks ◽  
Fuzzy Numbers ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Fuzzy Neural Networks ◽  
Dynamic Adjustment ◽  
Inference System ◽  
Fuzzy Neural ◽  
Interval Type

A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. A dynamic adjustment of the PSO allows the algorithm to behave better in the search for optimal results because the dynamic adjustment provides good synchrony between the exploration and exploitation of the algorithm. Results of experiments and a comparison between traditional neural networks and the fuzzy neural networks with interval type-2 fuzzy numbers weights using T-norms and S-norms are given to prove the performance of the proposed approach. For testing the performance of the proposed approach, some cases of time series prediction are applied, including the stock exchanges of Germany, Mexican, Dow-Jones, London, Nasdaq, Shanghai, and Taiwan.

System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

2013 ◽  
pp. 188-205
Author(s):  
Tsung-Chih Lin ◽  
Yi-Ming Chang ◽  
Tun-Yuan Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Recurrent Interval

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).

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