Artificial wavelet neuro-fuzzy model based on parallel wavelet network and neural network

Banakar, Ahmad; Azeem, Mohammad Fazle

doi:10.1007/s00500-007-0238-z

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…The nonlinear trend of a time series, x t , can be analysed at multiple scales through wavelet decomposition on the basis of the discrete wavelet transform (DWT). The DWT is defined taking discrete values of a and b (Banakar and Azeem, 2008). The full DWT for signal, x t , can be represented as (Mallat, 1989):…”

Section: Wavelet Analysismentioning

confidence: 99%

A comprehensive approach to characterization of the nonlinearity of runoff in the headwaters of the Tarim River, western China

Xu¹,

Li²,

Ji³

et al. 2009

Hydrological Processes

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Abstract:Nonlinear characteristics of the runoff processes in the headwaters of the Tarim River were identified and evaluated using several selected methods, including wavelet analysis, correlation dimension, and R/S analysis. Time-series of annual data describing runoff, average temperature, and precipitation from 1957 to 2005 were used to construct and test empirical models. The primary findings of this study were as follows: (1) The annual runoff of the headwaters are complex and nonlinear in nature, and they each presented periodic, nonlinear trends at the chosen time scales, chaotic dynamics, and long-memory characteristics. (2) These nonlinear trends appeared to have resulted from the regional climatic changes that occurred during the study period. The periodicity of changes in runoff occurred on an approximately 25-year cycle, which appeared to be correlated with temperature and precipitation cycles. In addition, the annual runoff exhibited a significant, positive correlation with the temperature and precipitation factors at the 4-, 8-, 16-, and 32-year temporal scales. (3) The correlation dimensions of the attractor derived from the runoff time series for the Hotan, Yarkand, and Aksu rivers were all greater than 3Ð0 and non-integral, implying that all three rivers are dynamic chaotic systems that are sensitive to initial conditions, and that the dynamic modelling of their annual runoff requires at least four independent variables. (4) The computed Hurst exponents indicate that a long-term memory characteristic exists in the annual runoff processes. However, there were some differences observed, with the Aksu and Yarkand rivers demonstrating a persistent trait, and the Hotan River exhibiting an anti-persistent feature.

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Section: Wavelet Analysismentioning

confidence: 99%

A comprehensive approach to characterization of the nonlinearity of runoff in the headwaters of the Tarim River, western China

Xu¹,

Li²,

Ji³

et al. 2009

Hydrological Processes

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“…This means that the convergence of WCAMC can be guaranteed (Banakar and Azeem 2007;Lee and Teng 2000;Lee 2004;Lee and Lin 2005). In addition, to obtain fast convergence, the adaptive optimal learning rate is derived as follows.…”

Section: Convergence Analysis For System Identificationmentioning

confidence: 99%

Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems

Lee

Wang

2008

Soft Comput

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This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey-Glass timeseries prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems.

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“…This WNN has m, p, n nodes in the input layer, hidden layer and output layer, respectively. And the activate function of the j-th node in the hidden layer is [1]…”

Section: Wavelet Neural Network 41 Structure Of Networkmentioning

confidence: 99%

“…In this paper, f (t) is chosen as sigmoid function: The wavelet neural network parameters in Fig. 3, (W (1) , W (2) , Θ (1) , Θ (2) , a 1 , . .…”

Section: Wavelet Neural Network 41 Structure Of Networkmentioning

confidence: 99%

Kinematics Analysis and Numerical Simulation of Hybrid Serial-Parallel Manipulator Based on Neural Network

Rahmani¹,

Gholami²,

Mahboubkhah³

2015

NNW

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This paper presents solution of kinematics analysis of a specific class of serial-parallel manipulators, known as 2-(6UPS) manipulators, which are composed of several modules consisting of elementary manipulators with the parallel structure of the Stewart platform, by artificial neural network. At first, the kinematics model of the hybrid manipulator is obtained. Then, as the inverse kinematics problem of this kind of manipulators is a very difficult problem to solve because of their highly nonlinear relations between joint variables and position and orientation of the end effectors, a wavelet based neural network (wave-net) with its inherent learning ability as a strong method was used to solve the inverse kinematics problem. Also, the proposed wavelet neural network (WNN) is applied to approximate the paths of middle and upper plate in a circular and a spiral path, respectively. The results show high accurate performance of the proposed WNN.

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Artificial wavelet neuro-fuzzy model based on parallel wavelet network and neural network

Cited by 9 publications

References 20 publications

A comprehensive approach to characterization of the nonlinearity of runoff in the headwaters of the Tarim River, western China

A comprehensive approach to characterization of the nonlinearity of runoff in the headwaters of the Tarim River, western China

Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems

Kinematics Analysis and Numerical Simulation of Hybrid Serial-Parallel Manipulator Based on Neural Network

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