Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods

Shoorehdeli, Mahdi Aliyari; Teshnehlab, Mohammad; Sedigh, Ali Khaki; Khanesar, Mojtaba Ahmadieh

doi:10.1016/j.asoc.2008.11.001

Cited by 123 publications

(73 citation statements)

References 34 publications

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“…Hence, this algorithm is employed in the present study to estimate Fr with the ANFIS network. In addition to these algorithms, the more recent use of hybrid ANFIS has led to improved ANFIS prediction results (Cus et al 2009;Shoorehdeli et al 2009;Chang et al 2011;Chen 2013;Bui et al 2016aBui et al , 2017b. Therefore, Differential Evolution (DE) is employed in this study and the results are compared with the hybrid algorithm results.…”

Section: Fourth Layermentioning

confidence: 99%

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Ebtehaj

Bonakdari

2017

Appl Water Sci

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Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number (Fr) parameters includes the volumetric sediment concentration (C V ), ratio of median particle diameter to hydraulic radius (d/R), ratio of median particle diameter to pipe diameter (d/D) and overall friction factor of sediment (k s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy (R 2 = 0.965) than the ANFIS model and regression-based equations.

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Section: Fourth Layermentioning

confidence: 99%

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Ebtehaj

Bonakdari

2017

Appl Water Sci

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“…In this study, whereas the parameters belonging to the membership functions found in the structure of ANFIS are optimized with PSO, a Kalman filter is used to find the values of the conclusion parameters. In another study conducted by Shoorehdeli et al [7], a new hybrid learning algorithm is proposed, where PSO is used for training the antecedent part and the forgetting factor recursive least squares algorithm is employed for training the conclusion part. Jalali-Heravi and Asadollahi-Baboli [8] suggested modified ant colony algorithm-based ANFIS training for the prediction of the inhibitory activity of quinazolinone derivatives on serotonin.…”

Section: Introductionmentioning

confidence: 99%

Training ANFIS by using the artificial bee colony algorithm

Karaboğa¹,

Kaya²

2017

Turk J Elec Eng & Comp Sci

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“…In addition, due to their dependence on the analytical derivatives, they are limited to specific objective functions, inferences, and MFs [2]. Some papers [1][2][3]25,26] have investigated the stability of fuzzy neural networks. The popular method for stability analysis is Lyapunov stability.…”

Section: Introductionmentioning

confidence: 99%

“…The convergence properties of such algorithms are discussed in [5,7,12,15,16,18], and [22]. Learning algorithms based on GD includes real-time recurrent learning (RTRL), ordered derivative learning and so on [1]. Derivative-based methods have the advantage of fast convergence, but they tend to converge to local minima [2].…”

Section: Introductionmentioning

confidence: 99%

“…The network model having a good function approximation capability through the training samples can well reflect the complex nonlinear relationship between objects [17]. However, one problem inherent within them is their convergence to local minima and the user set acceleration rates and inertia factor parameters that are sensitive to the learning process [1][2][3]. The FNNs with the BP learning algorithm have been used successfully in pattern recognition, optimization, classification, modeling, identification and controlling [13,31].…”

Section: Introductionmentioning

confidence: 99%

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Designing stable neural identifier based on Lyapunov method

Alibakhshi

Teshnehlab

Alibakhshi

et al. 2015

JAIDM

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The stability of learning rate in neural network identifiers and controllers is one of the challenging issues, which attract many researchers' interest in neural networks. This paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (MDNN) and studies the stability of this algorithm. Also, stable learning algorithm for parameters of MDNN is proposed. By the proposed method, some constraints are obtained for learning rate. Lyapunov stability theory is applied to study the stability of the proposed algorithm. The Lyapunov stability theory guaranteed the stability of the learning algorithm. In the proposed method, the learning rate can be calculated online and will provide an adaptive learning rate for the MDNN structure. Simulation results are given to validate the results.

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Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods

Cited by 123 publications

References 34 publications

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Training ANFIS by using the artificial bee colony algorithm

Designing stable neural identifier based on Lyapunov method

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