Differentiating adaptive Neuro-Fuzzy Inference System for accurate function derivative approximation

Khayat, Omid; Nejad, Hadi Chahkandi; Rahatabad, Fereidoun Nowshiravan; Abadi, Mahdi Mohammad

doi:10.1016/j.neucom.2012.09.025

Cited by 5 publications

(11 citation statements)

References 13 publications

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“…Weights and biases are determined during a training procedure by trainlm algorithm with 400 epochs. First order partial derivative of a 5-layer neurofuzzy model [15] is also used for the sake of comparison with three Gaussian membership functions for each input and 400 epochs of training. A three layer wavelet neural network with Mexican Hat wavelet function is also differentiated after the structures is constructed and the network parameters are made optimized by a gradient based back-propagation learning algorithm.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Fuzzy concept is an effective tool for dealing with complex nonlinear processes that are characterized with uncertain factors and ambiguities [3,[14][15][16]. A fuzzy wavelet neural network combines fuzzy logic with a wavelet neural network.…”

mentioning

confidence: 99%

“…In some applications approximating the first order derivative of a model is interested [15]. One can use a neural network-based model to approximate the function first.…”

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confidence: 99%

“…Then following the approximation procedure, mathematical computations may be used to approximate the partial derivatives. Khayat et al [15] proposed an ANFIS-based differentiator which can be used as a direct approximator of the first order partial derivative of a function based on the set of input-output pairs. In order to benefit the capability of wavelet neural networks in complex nonlinear system modeling and fuzzy concept in complex processes with uncertainty we introduce a fuzzy wavelet neural network (FWNN) structure for function approximation.…”

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confidence: 99%

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Performance Verification of a Fuzzy Wavelet Neural Network in the First Order Partial Derivative Approximation of Nonlinear Functions

Nejad

Farshad

Khayat

et al. 2015

Neural Process Lett

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Approximation of the first order partial derivative of a function modeled from a set of discrete data is the requirement of several applications. However, using a direct method for calculating the partial derivative from a set of discrete points is preferred rather differentiating the function which is obtained by modeling the discrete dataset. In this paper, the first order partial derivative of a fuzzy wavelet neural network structure is calculated to act as a direct differentiator. The structure of the network is described and its parameters are tuned by an adaptive gradient-based back propagation learning algorithm. It is shown that the proposed model outperforms the adaptive neuro-fuzzy inference-based and feed forward neural network-based differentiators in approximating the first order partial derivatives of multi-variable nonlinear functions.

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Section: Simulation Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

“…In some applications approximating the first order derivative of a model is interested [15]. One can use a neural network-based model to approximate the function first.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Performance Verification of a Fuzzy Wavelet Neural Network in the First Order Partial Derivative Approximation of Nonlinear Functions

Nejad

Farshad

Khayat

et al. 2015

Neural Process Lett

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“…ANFIS structure has been described, in details, in several researches (Khayat, Nejad, Rahatabad, & Abadi, ; Nejad, Farshad, Khayat, & Rahatabad, ; Nejad, Farshad, Rahatabad, & Khayat, ), whereas no comprehensive and descriptive studies on the WANFIS model can be found. The structure of interest has five layers consisting of (a) fuzzification layer, (b) fuzzy intersection layer, (c) rule firing normalization layer, (d) rule contribution calculation layer, and (e) wavelet‐based rule activation layer.…”

Section: Methodsmentioning

confidence: 99%

Performance evaluation of multilayer perceptron, radial basis function, fuzzy inference system, and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

Khayat

Afarideh

2019

Expert Systems

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Application of predictive models in industrial multiphase flow metering has attracted an increasing attention recently. Void fraction (VF), water–liquid ratio (WLR), and flow regime are key parameters, measured by oil/water/gas multiphase flow metres (MPFM) in petroleum industry. Artificial neural networks and fuzzy inference systems (FIS) are reliable and efficient computational models, which can be simply implemented on microprocessors of MPFMs, having the advantages of trainability, adaptability, and capability to model non‐linear functions. In this paper, a wavelet‐based adaptive neuro‐FIS (WANFIS) is introduced and validated by the prediction of multiphase flow measurement critical parameters including flow regime, VF, and WLR. The performance of the proposed WANFIS model is then compared with multilayer perceptron (MLP), radial basis function (RBF) network, and an FIS trained by fuzzy c‐means and a subtractive clustering method in the prediction of flow parameters in a customized designed structure of oil/water/gas MPFM. Structural parameters of all predictive models are first optimized to yield the most efficient structure for the available dataset. Comparison is then made between the optimized predictive models, in terms of mean squared error of parameter prediction, computation time, and repeatability of the prediction process. According to the obtained results, MLP model using Levenberg–Marquardt training algorithm and WANFIS model using gradient‐based back propagation dynamical iterative learning algorithm are the most efficient models, which give the best performance compared with other used models. All predictive models can predict the flow regime with 100% accuracy, whereas the highest inaccuracy is related to the prediction of WLR. The results of this study can be used to select and develop the most appropriate predictive model applicable in predicting and identifying flow measurement parameters in industrial MPFMs.

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CFNN: Correlated fuzzy neural network

Ebadzadeh

Salimi-Badr

2015

Neurocomputing

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Differentiating adaptive Neuro-Fuzzy Inference System for accurate function derivative approximation

Cited by 5 publications

References 13 publications

Performance Verification of a Fuzzy Wavelet Neural Network in the First Order Partial Derivative Approximation of Nonlinear Functions

Performance Verification of a Fuzzy Wavelet Neural Network in the First Order Partial Derivative Approximation of Nonlinear Functions

Performance evaluation of multilayer perceptron, radial basis function, fuzzy inference system, and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

CFNN: Correlated fuzzy neural network

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