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Characteristics Prediction Method of Electro-hydraulic Servo Valve Based on Rough Set and adaptive neuro-fuzzy inference system

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Chinese Journal of Mechanical Engineering 2010年第2期23卷 200-208页

作者： JIA Zhenyuan MA Jianwei WANG Fuji LIU Wei Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education Dalian University of Technology Dalian 116024 China

Synthesis characteristics of the electro-hydraulic servo valve are key factors to determine eligibility of the hydraulic production. Testing all synthesis characteristics of the electro-hydraulic servo valve after assembling leads to high repair rate and reject rate, so accurate prediction for the synthesis characteristics in the industrial production is particular important in decreasing the repair rate and the reject rate of the product. However, the research in forecasting synthesis characteristics of the electro-hydraulic servo valve is rare. In this work, a hybrid prediction method was proposed based on rough set（RS） and adaptive neuro-fuzzy inference system（ANFIS） in order to predict synthesis characteristics of electro-hydraulic servo valve. Since the geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve are from workers＇ experience, the inputs of the prediction method are uncertain. RS-based attributes reduction was used as the preprocessor, and then the exact geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve were obtained. On the basis of the exact geometric factors, ANFIS was used to build the final prediction model. A typical electro-hydraulic servo valve production was used to demonstrate the proposed prediction method. The prediction results showed that the proposed prediction method was more applicable than the artificial neural networks（ANN） in predicting the synthesis characteristics of electro-hydraulic servo valve, and the proposed prediction method was a powerful tool to predict synthesis characteristics of the electro-hydraulic servo valve. Moreover, with the use of the advantages of RS and ANFIS, the highly effective forecasting framework in this study can also be applied to other problems involving synthesis characteristics forecasting.

关键词： characteristics prediction rough set adaptive neuro-fuzzy inference system electro-hydraulic servo valve,artificial neural networks

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Column breakthrough studies for the removal and recovery of phosphate by lime-iron sludge:Modeling and optimization using artificial neural network and adaptive neuro-fuzzy inference system

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Chinese Journal of Chemical Engineering 2020年第7期28卷 1847-1859页

作者： Beverly S.Chittoo Clint Sutherland Project Management and Civil Infrastructure systems The University of Trinidad and TobagoSan Fernando CampusTarouba Link Road San FernandoWITrinidad and Tobago

Increases in the treatment of water to meet the growing water demand ultimately result in unmanageable quantities of residuals,the handling,and disposal of which is a major environmental ***,research into beneficial reuse of water treatment residuals continues *** study investigated the applicability of lime-iron sludge for phosphate adsorption by fixed-bed column ***-scale experiments were conducted at varying flow rates and bed *** and empirical models(Thomas,Yan,Bohart-Adams,Yoon-Nelson,and Wolboroska)as well as artificial intelligence techniques(Artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS))were used to simulate experimental breakthrough curves and predict column *** in flow rate resulted in reduced adsorption ***,adsorption capacity was not affected by bed *** was superior in predicting breakthrough curves and predicted breakthrough times with high accuracy(R^2>0.9962).Na OH(0.5 mol·L^-1)was successfully used to regenerate the adsorption *** nine cyclic adsorption/desorption runs,only a marginal decrease in adsorption and desorption efficiencies of 10%and 8%respectively was *** same regenerate Na OH solution was reused for all desorption *** nine cycles the eluent desorbed a total of 1550 mg phosphate exhibiting potential for further reuse.

关键词： Adsorption Phosphate Sludge adaptive neuro-fuzzy inference system Neural Network

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A reversibly used cooling tower with adaptive neuro-fuzzy inference system

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Journal of Central South University 2012年第3期19卷 715-720页

作者：吴加胜张国强张泉周晋郭永辉沈炜 College of Civil Engineering Hunan University

An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was *** field experimental work was carried out in order to gather enough data for training and *** statistical methods,such as the correlation coefficient,absolute fraction of variance and root mean square error,were given to compare the predicted and actual values for model *** simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite ***,the ANFIS approach can reliably be used for forecasting the performance of RUCT.

关键词： reversibly used cooling tower heating adaptive neuro-fuzzy inference system fuzzy modeling approach

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Inferring rules for adverse load combinations to crack in concrete dam from monitoring data using adaptive neuro-fuzzy inference system

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Science China(Technological Sciences) 2012年第1期55卷 136-141页

作者： XUHongZhong LI XueHong College of Transportation Science and Engineering Nanjing University of Technology Nanjing 210009 China

The formation and growth of cracks in concrete dams are mainly induced by hydrostatic and temperature *** cracks es-pecially unstable cracks are of great danger to the safety of dams,it is critical to avoid extremely adverse load combinations during the dam operations to achieve the stability of ***,the adverse load combinations have to be deter-mined empirically by experts based on specific dam site ***,it is attractive to apply quantitative instead of empirical methods to identify the adverse loading *** this study,we employ an adaptive neuro-fuzzy inference sys-tem(ANFIS) to Chencun concrete *** ANFIS is able to help us build a relationship between the model inputs(reservoir water level and air temperature) and the model output(crack opening displacement).Based on this relationship,the rules of the adverse load combinations to the crack are generated directly from the monitoring *** accuracy of the trained ANFIS is proved by comparing the modeling results and the monitoring *** work demonstrates that the ANFIS is a useful ap-proach for accurately recognizing the rules of the adverse load combinations that can be used in the knowledge base of dam safety expert system.

关键词： crack concrete dam load combination adaptive neuro-fuzzy inference system rules

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An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores

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Petroleum 2023年第4期9卷 629-646页

作者： Chibuzo Cosmas Nwanwe Ugochukwu Ilozurike Duru Department of Minerals and Petroleum Resources Engineering Technology Federal Polytechnic NekedeOwerriP.M.B.1036OwerriNigeria Department of Petroleum Engineering Federal University of TechnologyOwerriP.M.B.1526OwerriNigeria

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are *** is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental *** addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical *** makes it difficult for other readers to use these ML models since the datasets used in their development are not *** study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)*** dataset was divided into two sets;80%for training and 20%for *** performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction *** optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter *** analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on *** addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning *** training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.

关键词： Machine learning models Empirical correlations Mechanistic models Multiphase flowing bottom-hole pressure adaptive neuro-fuzzy inference system White-box model

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Force tracking control for electrohydraulic servo system based on adaptive neuro-fuzzy inference system(ANFIS)controller

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International Journal of Intelligent Computing and Cybernetics 2021年第1期14卷 1-16页

作者： Lie Yu Lei Ding Fangli Yu Jianbin Zheng Yukang Tian School of Electronic and Electrical Engineering Wuhan Textile University–Yangguang CampusWuhanChina School of Information Engineering Wuhan University of TechnologyWuhanChina

Purpose–The purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system(EHSS).Specifically,the adaptive neuro-fuzzy inference system(ANFIS)is selected to improve the control performance for ***/methodology/approach–Two types of input–output data were chosen to train the ANFIS *** inputs are the desired and actual forces,and the output is the *** first type is to set a sinusoidal signal for the current to produce the actual driving force,and the desired force is chosen as same as the actual *** other type is to give a sinusoidal signal for the desired *** the action of the PI controller,the actual force tracks the desired force,and the current is the output of the PI ***–The models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II *** results reveal that the ANFIS I controller possesses the best performance in terms of overshoot,rise time and mean absolute error and show adaptivity to different tracking conditions,including sinusoidal signal tracking and sudden change signal ***/value–This paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

关键词： Force tracking control Electrohydraulic servo system adaptive neuro-fuzzy inference system PI controller

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Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system

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The Journal of Biomedical Research 2017年第5期31卷 419-427页

作者： Mahdi Alizadeh Omid Haji Maghsoudi Kaveh Sharzehi Hamid Reza Hemati Alireza Kamali Asl Alireza Talebpour Department of Bioengineering Temple University Philadelphia PAl9121 USA Department of Medicine Section of Gastroenterology School of Medicine Temple University Philadelphia PA 19140. USA Department of Radiation Medicine Engineering ShahidBeheshti University Tehran *** Iran. Department of Electrical and Computer Engineering ShahidBeheshti University Tehran *** Iran.

Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more *** purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures(contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.

关键词： adaptive neuro-fuzzy inference system co-occurrence matrix wireless capsule endoscopy texture feature

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Cancerous cell viability affected by synergism between electric pulses and a low dose of silver nanoparticle:An adaptive neuro-fuzzy inference system

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Medicine in Novel Technology and Devices 2024年第1期21卷 45-52页

作者： Salim Mirshahi Behzad Vahedi Kiarash Aryana Ameneh Sazgarnia Department of Mechanical Engineering Sharif University of TechnologyTehranIran Medical Physics Research Center Mashhad University of Medical SciencesMashhadIran Functional neurosurgery Research Center Shohada Tajrish Comprehensive Neurosurgical Center of ExcellenceShahid Beheshti University of Medical SciencesTehranIran Department of Mechanical Engineering Ferdowsi University of MashhadMashhadIran

In the current study,applying simultaneously electroporation and silver nanoparticles(SNPs)are ***,one restriction normally assigned to such nanoparticles is their side effects on the vital organs of the *** mitigate such deleterious effects,it is better to use lower dosages of ***,this can result in a decline in the technique's *** compensate for the lower dose of SNPs,one can use secondary method like electroporation to deliver SNPs directly into the cells and reinforces the effect of electric pulses due to the high electrical conductivity of SNPs while having a minimal cytotoxicity effect on normal cells that are not treated with *** the present study,synergism effects of both procedures(SNPs and electroporation)experimentally and theoretically are considered to investigate the property of each technique in increasing the performance with respect to both procedures'*** investigate more,adaptive neuro-fuzzy inference system(ANFIS)is used to predict the percent cell viability of cancerous cells affected by both procedures by considering amplitude and duration as inputs affecting on change of cell viability as an *** results obtained from both experimental and simulation procedures showed that the maximum synergism between nanoparticles and electric pulses was recorded at 700V/cm strength and 100μs ***,Results indicated high correlation between observed and predicted data(r2=0.88).Moreover,the calculated root mean square error for the results of the ANFIS model was equal to *** implies that the model has practical value and can estimate the percent cell viability of cancerous cells influenced by both procedures with varying electric field amplitude and *** method can be proposed for other biophysical or drug delivery applications to save time and resources by utilizing the previous experimental data rather than performing more experiments.

关键词： Electroporation Cancer cell adaptive neuro-fuzzy inference system Nanoparticle Drug delivery

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Composition Estimation of Reactive Batch Distillation by Using adaptive neuro-fuzzy inference system

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Chinese Journal of Chemical Engineering 2010年第4期18卷 703-710页

作者： S.M.Khazraee A.H.Jahanmiri School of Chemical and Petroleum Engineering Shiraz UniversityShiraz 71345Iran

Composition estimation plays very important role in plant operation and *** Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and *** this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS) ,which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch *** state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction *** mathematical model is verified by pilot plant *** simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch *** estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.

关键词： reactive batch distillation multicomponent pilot plant adaptive neuro-fuzzy inference system state estimation

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Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

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International Journal of Mining Science and Technology 2020年第6期30卷 785-797页

作者： Ahmet Teymen Engin Cemal Mengüç Department of Mining Engineering Nigde Ömer Halisdemir University51240 NigdeTurkey Department of Electrical and Electronics Engineering Nigde Ömer Halisdemir University51240 NigdeTurkey

In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were *** predict the UCS,simple regression(SRA),multiple regression(MRA),artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and genetic expression programming(GEP)have been *** obtained UCS values were compared with the actual UCS values with the help of various *** were modeled using different methods and compared with each *** the study where the performance indice PIat was used to determine the best performing method,MRA method is the most successful method with a small *** is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA,while these values are 2.44,2.33,and 2.22 for GEP,ANFIS,and ANN techniques,*** results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with *** to the performance index assessment,the weakest model among the nine model is P7,while the most successful models are P2,P9,and P8,respectively.

关键词： Uniaxial compressive strength adaptive neuro-fuzzy inference system Multiple regression Artificial neural network Genetic expression programming

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