Fuzzy Wavelet Neural Network With an Accelerated Hybrid Learning Algorithm

Davanipoor, M.; Zekri, Maryam; Sheikholeslam, Farid

doi:10.1109/tfuzz.2011.2175932

Cited by 104 publications

(43 citation statements)

References 18 publications

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“…Here FWNN with only two rules has been constructed. Then the network is trained by the algorithm introduced in [16]. After the learning phase, the below input is used to motivate the trained network: …”

Section: Examplementioning

confidence: 99%

Nonlinear model predictive control based on fuzzy wavelet neural network and chaos optimization

Davanipour¹,

Asadipooya²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this paper a combined controller is proposed for nonlinear dynamical systems. The controller is constructed by a fuzzy wavelet network and nonlinear model predictive control. Chaotic optimization, which is fast and robust, is applied to generate optimized controlled input in nonlinear model predictive control. The ability of the fuzzy wavelet neural network and the proposed controller is shown by simulation. It is illustrated that the proposed method is able to increase the speed of tracking in addition to having very little steady state error. Using chaotic optimization makes the controller robust in the presence of noise.

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Section: Examplementioning

confidence: 99%

Nonlinear model predictive control based on fuzzy wavelet neural network and chaos optimization

Davanipour¹,

Asadipooya²

2017

Turk J Elec Eng & Comp Sci

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“…The algorithm goes on for the rest of wavelets. Since all remaining wavelets are made orthogonal to all selected ones in each step of the algorithm, the improvement of each selectable wavelet is isolated [37]. After employing this stage, wavelet network is constructed as where s is the number of wavelons in the hidden layer and w i is the weight of wave l on s. After performing the OLS algorithm, W is composed of ortho normal matrix N and upper triangular matrix A.…”

Section: Wavelet Neuronsmentioning

confidence: 99%

Dimensionality Reduction of Optical Coherence Tomography Images for the Early Diagnosis of Alzheimer’s Disease

Sandeep¹,

A²,

Mahadevan³

et al. 2017

AJEEE

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Alzheimer's disease (AD) is the most common cause of dementia and its incidence is increasing worldwide along with population aging. Previous clinical and histologic studies suggest that the neurodegenerative process, which affects the brain, may also affect the retina of AD patients. In this case, the employment of advanced Biomedical Engineering Technology will definitely helpful for making diagnosis. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer's kind pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. Profiling of human body parameter using computers can be utilised for the early Diagnosis of Alzheimer's disease. There are lot of tests and imaging modalities to be performed for an effective Diagnosis of the disease. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET), Single Photon Emission CT Scanning (SPECT) and Optical Coherence Tomography (OCT).In the recent studies made on Alzheimer's disease it is clearly investigated that are some parameter changes on the retina of the eye of the AD patients. In this research we have proposed a new scheme based on Wavelet Networks (WN) for the dimensionality reduction of OCT retinal images for the early Diagnosis of AD.

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“…ji ðx ri À c ji Þ 2 and it is the Mexican Hat wavelet function (Abiyev and Kaynak, 2008;Yilmaz and Oysal, 2010;Hsu, 2011;Davanipoor et al, 2012).…”

Section: Recurrent Fuzzy Wavelet Neural Networkmentioning

confidence: 99%

“…In recent years, Fuzzy WNNs (FWNNs) have been presented in some application areas (Ho et al, 2001;Abiyev and Kaynak, 2008;Yilmaz and Oysal, 2010;Lu, 2011;Hsu, 2011;Davanipoor et al, 2012;Bodyanskiy and Vynokurova, 2013). The FWNNs, the combination of fuzzy concept and the WNNs, can bring the low level learning and good computational capability of the WNNs into fuzzy system and also high humanlike IF-THEN rule thinking and reasoning of fuzzy system into the WNNs.…”

Section: Introductionmentioning

confidence: 99%

“…By tuning the shape of the membership functions, the approximating process of the FWNNs could be improved without increasing the number of wavelet bases. Some researchers (Abiyev and Kaynak, 2008;Yilmaz and Oysal, 2010;Lu, 2011;Davanipoor et al, 2012;Bodyanskiy and Vynokurova, 2013) proposed the FWNNs for identification-control, and used the gradient method to update/adjust the parameters of the networks (translations, dilations of fuzzy wavelet functions and weights of networks). The results of these methods have good performance and achieve higher precision than FNNs or WNNs methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive motion/force control strategy for non-holonomic mobile manipulator robot using recurrent fuzzy wavelet neural networks

Wang

Long

Mao

2014

Engineering Applications of Artificial Intelligence

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Fuzzy Wavelet Neural Network With an Accelerated Hybrid Learning Algorithm

Cited by 104 publications

References 18 publications

Nonlinear model predictive control based on fuzzy wavelet neural network and chaos optimization

Nonlinear model predictive control based on fuzzy wavelet neural network and chaos optimization

Dimensionality Reduction of Optical Coherence Tomography Images for the Early Diagnosis of Alzheimer’s Disease

Adaptive motion/force control strategy for non-holonomic mobile manipulator robot using recurrent fuzzy wavelet neural networks

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