Adaptive fuzzy wavelet network control design for nonlinear systems

“…[29][30][31][32][33]). In many applications, WNN plays the role of adaptive network that uses adaptive laws for online tuning of its parameters [28][29][30]. Discontinuity and nonlinearity are commonly happening in environments that interact with robots.…”

Section: Introductionmentioning

confidence: 99%

Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

Dehghan

Danesh

Sheikholeslam

et al. 2015

Applied Soft Computing

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“…A good initialization of the parameters of the FWNN enables to obtain fast convergence. A number of methods are proposed in literature for initialization of the wavelets, such as the orthogonal least square procedure [7,8] and the clustering method [12]. An optimal initial choice of the dilation and the translation parameters of the wavelets increases the training speed and results in fast convergence.…”

Section: Learning Algorithmmentioning

confidence: 99%

“…Recently, this combination was applied in function approximation, identification and control [7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

The preference of Fuzzy Wavelet Neural Network to ANFIS in identification of nonlinear dynamic plants with fast local variation

Davanipour

Zekri

Sheikholeslam

2010

2010 18th Iranian Conference on Electrical Engineering

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This paper presents a Fuzzy Wavelet Neural Network (FWNN) for identification of a system with fast local variation. The FWNN combines wavelet theory with fuzzy logic and neural networks. An effective clustering algorithm is used to initialize the parameters of the FWNN. Learning fuzzy rules in this FWNN is based on gradient decent method. The performance of the FWNN structure is illustrated by applying to a nonlinear dynamic plant which has fast local variation then compared with Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Simulation results indicate remarkable capabilities of the proposed identification method for plants with fast local variation.

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“…There may be one of only few cases of application for control field that Maryam Zekri, et.al. [9] proposed a controller using both wavelet neural network and neural network which has high approximation ability. However it needs a number of times of offline learning of connection weights and parameters of NN.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control System Based on Self-Organizing Wavelet Neural Network with H8 Tracking Performance Compensator

Obayashi

Kamikariya

Uchiyama

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Wavelet neural network (WNN) has high function approximation capability, because it consists of neurons, each of which has a localized and vibratory waveform, and the center of the waveform and its scaling and spatial extent/reduction are adjustable. Therefore it has outstanding ability to adapt to changes of environments. In the field of control engineering, Neural Network (NN) and Fuzzy Neural Network (FNN) are often used as a tool of nonlinear control system design. However it is seldom seen that WNN is used for control system designs. There may be one of only few cases that WNN is used as controller whose structure is furthermore fixed and it requires off-line learning to design the control system. In such case, it is difficult to react to change in the environment. So, we propose an adaptive wavelet neural network control system based on WNN with an adaptable self-organizing network structure and with H tracking performance compensator to be robust. In addition, we prove stability of the proposed system by Lyapunov stability analysis. Finally, through inverted pendulum control simulations, we showed the proposed system is superior to other conventional control systems.

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Adaptive fuzzy wavelet network control design for nonlinear systems

Cited by 70 publications

References 25 publications

Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

The preference of Fuzzy Wavelet Neural Network to ANFIS in identification of nonlinear dynamic plants with fast local variation

Adaptive Control System Based on Self-Organizing Wavelet Neural Network with H8 Tracking Performance Compensator

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