On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory

Becerikli, Yaşar

doi:10.1007/s00521-004-0429-9

Cited by 23 publications

(20 citation statements)

References 50 publications

(79 reference statements)

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“…Then the sigmoid function was used as an activation function in the output layer. (Becerikli, 2004) proposes a network with unconstrained connectivity and with dynamic elements (lag dynamics) in its wavelet processing units called dynamic WN.…”

Section: Structure Of a Wavelet Networkmentioning

confidence: 99%

Wavelet Neural Networks: A Practical Guide

Alexandridis

Zapranis

2011

SSRN Journal

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Abstract-Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.

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Section: Structure Of a Wavelet Networkmentioning

confidence: 99%

Wavelet Neural Networks: A Practical Guide

Alexandridis

Zapranis

2011

SSRN Journal

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“…In this example, the proposed fuzzy system and Sugeno fuzzy system performances are compared for a given a single variable a nonlinear piecewise-continuous scalar function or discrete event function [8,16,20].…”

Section: Example 1: Function Approximationmentioning

confidence: 99%

Fuzzy rule-base driven orthogonal approximation

Alcı

2007

Neural Comput Applic

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In this study, orthogonal approximation concept is applied to fuzzy systems. We propose a new useful model adapted from the well-known Sugeno type fuzzy system. The proposed fuzzy model is a generalization of the zeroorder Sugeno fuzzy system model. Instead of linear functions in standard Sugeno model, we use nonlinear functions in the consequent part. The nonlinear functions are selected from a trigonometric orthogonal basis. Orthogonal function parameters are trained along with the Sugeno fuzzy system. The proposed model is demonstrated using three simulations-a nonlinear piecewise-continuous scalar function modeling and filtering, nonlinear dynamic system identification, and time series prediction. Finally some performance comparisons are carried out.

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“…The partial derivative of the cost function with θ is computed using equation (11). (12) θ t shows parameters and θ t+1 shows updated parameters. µ is the learning rate for equation (12).…”

Section: Structure Of Wavenetmentioning

confidence: 99%

“…(12) θ t shows parameters and θ t+1 shows updated parameters. µ is the learning rate for equation (12). In this study momentum has been used which increases the learning rate with out an oscillation.…”

Section: Structure Of Wavenetmentioning

confidence: 99%

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Comparison of Wavenet and Neuralnet for System Modeling

Postalcioglu

Erkan

Bolat

2005

Lecture Notes in Computer Science

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Abstract. This paper presents nonlinear static and dynamic system modeling using wavenet and neuralnet. Wavenet combines wavelet theory and feedforward neuralnet, so learning approach is similar to neuralnet. The selection of transfer function is crucial for the approximation property and the convergence of the network. The purelin and the tansig functions are used as the transfer functions for neuralnet and the first derivative of a gaussian function is used as the transfer function for wavenet. Wavenet and neuralnet parameters are optimized during learning phase. Selecting all initial values random, but for wavenet, it may be unsuitable for process modeling because wavelets have localization feature. For this reason heuristic procedure has been used for wavenet. In this study gradient methods have been applied for parameters updating with momentum. Error minimization is computed by quadratic cost function for wavenet and neuralnet. Nonlinear static and dynamic functions have been used for the simulations. Recently wavenet has been used as an alternative of the neuralnet because interpretation of the model with neuralnet is so hard. For wavenet learning approach, training algorithms require smaller number of iterations when compared with neuralnet. Consequently, according to the number of training iteration and TMSE, dynamic and static system modeling with wavenet is better as shown in results.

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On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory

Cited by 23 publications

References 50 publications

Wavelet Neural Networks: A Practical Guide

Wavelet Neural Networks: A Practical Guide

Fuzzy rule-base driven orthogonal approximation

Comparison of Wavenet and Neuralnet for System Modeling

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