Application of adaptive neuro-fuzzy controller for SRM

Akçayol, M. Ali

doi:10.1016/j.advengsoft.2004.03.005

Cited by 114 publications

(27 citation statements)

References 9 publications

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“…The basic idea behind the soft computing methodology is to collect input/output data pairs and to learn the proposed network from these data. This technique gives fuzzy logic the capability to adapt the membership function parameters that best allow the associated FIS to track the given input/output data (Wahida Banu et al 2011;Grigorie and Botez 2009;Ali Akcayol 2004). Experimental investigation is carried out to extract the training and checking data for the ANFIS network.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

et al. 2014

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Scouring around bridge pier is a major cause of bridge instability. Thus, providing appropriate methods in order to reduce and control the scour depth have received much attention. Using a slot in the bridge piers is one of modern methods of reducing bridge local scouring. In the present study, the effects of a rectangular slot on local scour mitigation around bridge pier groups have been investigated with adaptive neurofuzzy (ANFIS) method. ANFIS shows very good learning and prediction capabilities, which makes it an efficient tool to deal with encountered uncertainties in any system like scouring. The results show that the scour depth increased in the first pier by reinforcing effect and it decreased in the rear piers because of sheltering effect in compare with single pier. In addition, application of the slot in pier groups leads to an increase in the impact of reinforcing effect and reduce the influence of sheltering effect.The use of slot is more influential in front bridge piers than the rear piers in pier groups with 4D distance, however, this effectiveness doesn't have significant difference among the piers with 2D distance and same as single pier. Laboratory experiments were conducted to create experimental training and checking data for ANFIS network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

et al. 2014

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“…The ANFIS is one of the methods to organize the fuzzy inference system with given input/output data pairs [24,25]. This technique gives fuzzy logic the capability to adapt the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data [26,27].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Petković

Shamshirband

Anuar

et al. 2015

J Intell Robot Syst

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The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

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“…It uses ANN's theory to determine fuzzy inference properties from data samples. ANFIS is a specific method in neuro-fuzzy with improved estimation speed, simplicity, error free, and [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Diffusion coefficient prediction of acids in water at infinite dilution by QSPR method

Khajeh

Rasaei

2011

Struct Chem

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In this article, quantitative structure-property relationship (QSPR) models were developed for estimation of diffusion coefficients (DCs) of acids at infinite dilution in water. These models were obtained based on molecular descriptors of a set of 65 compounds at 298.15 K. Five date splits are randomly extracted from this dataset. Using a genetic algorithm (GA) variable-selection approach, five molecular descriptors in each split were selected from a set of thousand of them. To analyze the nonlinear behavior of these molecular descriptors, Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function neural network (RBF NN) were used. These models were trained on 52 arbitrary sets of the data, and the remaining 13 sets were employed to evaluate the proposed models. Genetic function approximation (GFA) method could predict DCs of test dataset with high value of squared correlation coefficients of 0.984. QSPR model even gave superior predictions. ANFIS and RBF NN methods when implemented in QSPR model provided the squared correlation coefficients of 0.989 and 0.987, respectively. It is therefore suitable to be used in the proposed QSPR models, especially when applied with AN-FIS method, to obtain DC of acids at infinite dilution in water instead of other expensive and complicated experiments.

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Application of adaptive neuro-fuzzy controller for SRM

Cited by 114 publications

References 9 publications

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Diffusion coefficient prediction of acids in water at infinite dilution by QSPR method

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