Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study

Abiyev, Rahib H.; Kaynak, Okyay

doi:10.1109/tie.2008.924018

Cited by 229 publications

(105 citation statements)

References 22 publications

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“…4 and the parameters of the linear functions of (4) in fourth and fifth layers. To design FNS classifier, the training of the membership functions parameters c ij (t) and  ij (t) (i=1,..,m, j=1,..,r) in the premise part and parameter values of the w jk (t), a ij (t), b j (t) (i=1,..,m, j=1,..,r, k=1,..,n) in consequent part is carried out [28,31]. In the paper, we applied fuzzy clustering and gradient algorithms for the update of FNS parameters [33].…”

Section: A Parameter Learningmentioning

confidence: 99%

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Intelligent Classification of Liver Disorder using Fuzzy Neural System

Ma’aitah¹,

Abiyev²,

Bush³

2017

ijacsa

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Abstract-In this study, designed an intelligent model for liver disorders based on Fuzzy Neural System (FNS) models is considered. For this purpose, fuzzy system and neural networks (FNS) are explored for the detection of liver disorders. The structure and learning algorithm of the FNS are described. In this study, we utilized dataset extracted from a renowned machine learning data base (UCI) repository. 10 folds crossvalidation approach was explored for the design of the system. The designed algorithm is accurate, reliable and faster as compared to other traditional diagnostic systems. We highly recommend this framework as a specialized training tool for medical practitioners.

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Section: A Parameter Learningmentioning

confidence: 99%

“…This section calculates, the membership degree to which input value belongs in fuzzy set and for each input signal entering the system. We use Gaussian membership function to describe linguistic terms [31].…”

mentioning

confidence: 99%

Intelligent Classification of Liver Disorder using Fuzzy Neural System

Ma’aitah¹,

Abiyev²,

Bush³

2017

ijacsa

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“…Furthermore, for the mechanical press, unknown high impact loading exists in the stamping stage, an intelligent control approach should be used to compensate this disturbance. Many researches about fuzzy control (FC), [15][16][17] neural network control (NNC), [18][19][20] and iterative learning control (ILC) 21 have been studied for adaptive compensators and dynamic observers. The radial basis function neural network (RBFNN), which has simple structure and fast convergence speed, [22][23][24] is one of the most effective intelligent compensation methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-loop servo control system of double toggle mechanical press for flexible forming process based on sliding mode control and neural network techniques

Liang

Zhao

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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A hybrid-loop servo control system based on sliding-mode control and neural network control is proposed in this article to realize flexible forming process on a double toggle mechanical press. First, kinematics and dynamic analysis of the drive system are conducted to derive the control objective behavior. Then, the structure of the hybrid-loop system is introduced; sliding-mode control is applied to compensate the punch position errors. In the inner loop of the servo motor, complementary sliding-mode control is used to track the motor rotation angle, and a radial basis function neural network estimator is applied to eliminate the considerable load disturbances in the stamping stage. Finally, experimental hardware is constructed and a compound blanking and drawing process is carried out to validate the proposed servo control system in practice. With different materials and drawing depth, high tracking accuracy and robustness are exhibited to realize favorable forming performance.

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“…Hybrid wavelet-neuro-fuzzy systems [3], [4], [5], [6] emerged as the synergism of these three directions in computational intelligence. The wavelet-neuro-fuzzy systems possess the learning capabilities similar to those of neural networks, provide the interpretability and transparency of results inherent to the fuzzy approach and similarly effective wavelet systems for non-stationary signal processing with local features.…”

Section: Introductionmentioning

confidence: 99%

Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

Bodyanskiy

Pliss

Vynokurova

2013

Information Technology and Management Science

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-In the paper, a new flexible modification of neofuzzy neuron, neuro-fuzzy network based on these neurons and adaptive learning algorithms for the tuning of their all parameters are proposed. The algorithms are of interest because they ensure the on-line tuning of not only the synaptic weights and membership function parameters but also forms of these functions that provide improving approximation properties and allow avoiding the occurrence of "gaps" in the space of inputs.

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Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study

Cited by 229 publications

References 22 publications

Intelligent Classification of Liver Disorder using Fuzzy Neural System

Intelligent Classification of Liver Disorder using Fuzzy Neural System

Hybrid-loop servo control system of double toggle mechanical press for flexible forming process based on sliding mode control and neural network techniques

Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

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