Generalized type-2 fuzzy weight adjustment for backpropagation neural networks in time series prediction

Gaxiola, Fernando; Melín, Patricia; Valdez, Fevrier; Castillo, Oscar

doi:10.1016/j.ins.2015.07.020

Cited by 59 publications

(20 citation statements)

References 52 publications

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“…Artificial neuron, which is a basic processing unit of the neural network [23], connects with each other and accepts a weighted input to produce a corresponding output by means of an activation function [24]. At present, ANN mostly adopts M-P model which was put forward by psychologists Mcculloch and mathematical logician Pitts.…”

Section: Artificial Neuron Modelmentioning

confidence: 99%

Application of artificial neural network in the diagnostic system of osteoporosis

Xiang

2016

Neurocomputing

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Section: Artificial Neuron Modelmentioning

confidence: 99%

Application of artificial neural network in the diagnostic system of osteoporosis

Xiang

2016

Neurocomputing

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“…The proposed method in the present research is different to other papers, such as in Gaxiola et al [7,8], where the fuzzy weights are obtained using interval type-2 fuzzy inference systems in [7] and generalized type-2 fuzzy inference systems in [8] for the connections between the layers, and without any changes for obtaining the change of the weights for each epoch of the backpropagation algorithm.…”

Section: Introductionmentioning

confidence: 93%

Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks

et al. 2017

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Abstract:A comparison of different T-norms and S-norms for interval type-2 fuzzy number weights is proposed in this work. The interval type-2 fuzzy number weights are used in a neural network with an interval backpropagation learning enhanced method for weight adjustment. Results of experiments and a comparative research between traditional neural networks and the neural network with interval type-2 fuzzy number weights with different T-norms and S-norms are presented to demonstrate the benefits of the proposed approach. In this research, the definitions of the lower and upper interval type-2 fuzzy numbers with random initial values are presented; this interval represents the footprint of uncertainty (FOU). The proposed work is based on recent works that have considered the adaptation of weights using type-2 fuzzy numbers. To confirm the efficiency of the proposed method, a case of data prediction is applied, in particular for the Mackey-Glass time series (for τ = 17). Noise of Gaussian type was applied to the testing data of the Mackey-Glass time series to demonstrate that the neural network using a interval type-2 fuzzy numbers method achieves a lower susceptibility to noise than other methods.

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“…They are widely used in many applications due to their ability to learn from input-output data. The combination of FLC and ANN, called fuzzy neural network (FNN), fuses the reasoning ability of FLC to handle uncertain information with the training capability of ANN to learn from the controlled process (Gaxiola et al, 2015). FNN has shown promising results as it adopts the advantages from both FLC and ANN (Wang et al, 2015;Kim and Chwa, 2015;Gaxiola et al, 2014).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

Sarabakha¹,

Imanberdiyev²,

Kayacan

et al. 2017

Information Sciences

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In this paper, a Levenberg-Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor's control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg-Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions.

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Generalized type-2 fuzzy weight adjustment for backpropagation neural networks in time series prediction

Cited by 59 publications

References 52 publications

Application of artificial neural network in the diagnostic system of osteoporosis

Application of artificial neural network in the diagnostic system of osteoporosis

Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

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