A type-2 fuzzy wavelet neural network for system identification and control

Abiyev, Rahib H.; Kaynak, Okyay; Kayacan, Erdal

doi:10.1016/j.jfranklin.2013.04.020

Cited by 55 publications

(22 citation statements)

References 43 publications

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“…In this paper, the Morlet wavelet is adopted as an activation function in the hidden nodes because, in comparison to the broader Mexico hat wavelet, orthogonal wavelet, and Gauss spine wavelet, the Morlet wavelet has the smallest error and the best computational stability [33]. The formula is given below:…”

Section: Wavelet Neural Network (Wnn)mentioning

confidence: 99%

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

2016

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Abstract:Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO) determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals) of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method.

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Section: Wavelet Neural Network (Wnn)mentioning

confidence: 99%

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

2016

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“…The output of the master (Y m ) and slave (Y S ) manipulators are rotation angle ( ) and: (7)   Human operator controls the master manipulator by force F h where the slave manipulator can implement the given task remotely. The force F h , applied by the human operator, is not predictable.…”

Section: Ii1 Teleoperation Systemsmentioning

confidence: 99%

“…The wide practical use of T2FWNN systems is the motivation behind studies to develop improvements [48]. Rahib [7] introduced an ordinary type-2 FWNN.…”

Section: Ii2 Structure Of Proposed Type-2 Fuzzy Wavelet Networkmentioning

confidence: 99%

A Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system

Ganjefar

Solgi

2015

Information Sciences

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“…However, type-2 fuzzy logic systems give better results in many areas. They have been applied to many different applications such as identification of nonlinear systems [16][17][18][19][20][21], control [22,23], time series prediction [24], system modeling [20,25,26], stock price prediction [27] and control of mobile robots [28,29]. In [30], a review of type-2 fuzzy logic applications is presented for pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Multiplication Wavelet Neural Network Model Design for Classification of Breast Cancer

Gündüz

2018

ijarcs

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Breast cancer is one of the most common types of cancer. For this reason, it is very important to diagnose breast cancer. In this paper, a type-2 fuzzy multiplication wavelet neural network model is proposed to classify the Wisconsin Breast Cancer dataset. In this model, Shannon wavelet function is used as the type-2 membership function and the multiplication of the Shannon wavelet functions is used in the conclusion part of the rules. The results of proposed model is compared with type-1 fuzzy multiplication wavelet neural network, multilayer perceptron network, radial basis function network, Bayesian network learning, and decision tree algorithm. It can be seen that proposed type-2 fuzzy multiplication wavelet neural network model gives the best results among these algorithms.

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A type-2 fuzzy wavelet neural network for system identification and control

Cited by 55 publications

References 43 publications

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

A Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system

Type-2 Fuzzy Multiplication Wavelet Neural Network Model Design for Classification of Breast Cancer

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