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Investigation of complex modulus of base and EVA modified bitumen with adaptive-network-based fuzzy inference system

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EXPERT systemS WITH APPLICATIONS 2011年第1期38卷 969-974页

作者： Yilmaz, Mehmet Kok, Baha Vural Sengoz, Burak Sengur, Abdulkadir Avci, Engin Firat Univ Fac Engn Dept Civil Engn TR-23119 Elazig Turkey Dokuz Eylul Univ Fac Engn Dept Civil Engn TR-35160 Izmir Turkey Firat Univ Dept Elect & Comp Educ TR-23119 Elazig Turkey

This study aims to model the complex modulus of base and ethylene-vinyl-acetate (EVA) modified bitumen by using adaptive-network-based fuzzy inference system (ANFIS). The complex modulus of base and EVA polymer modified bitumen (PMB) samples were determined using dynamic shear rheometer (DSR). PMB samples have been produced by mixing a 50/70 penetration grade base bitumen with EVA copolymer at five different polymer contents. In ANFIS modeling, the bitumen temperature, frequency and EVA content are the parameters for the input layer and the complex modulus is the parameter for the output layer. The hybrid learning algorithm related to the ANFIS has been used in this study. The variants of the algorithm used in the study are two input membership functions and three input membership functions for each of the all inputs. The input membership functions are triangular, gbell, gauss2, and gauss. The results showed that EVA polymer modified bitumens display reduced temperature susceptibility than base bitumens. In the light of analysis the adaptive-network-based fuzzy inference system and statistical methods can be used for modeling the complex modulus of bitumen under varying temperature and frequency. The analysis indicated that the training accuracy is improved by decreasing the number of input membership functions and the utilization of the two gauss input membership functions appeared to be most optimal topology. Besides, it is realized that the predicted complex modulus is closely related with the measured (actual) complex modulus. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Bitumen Ethylene-vinyl-acetate Complex modulus adaptive-network-based fuzzy inference system

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AN adaptive-network-based fuzzy inference system FOR PREDICTING SPRINGBACK OF U-BENDING

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2013年第3期37卷 335-344页

作者： Lin, Bor-Tsuen Huang, Kun-Min Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung Taiwan Met Ind Res & Dev Ctr Kaohsiung Taiwan

Springback will occur when the external force is removed after bending process in sheet metal forming. This paper proposed an adaptive-network-based fuzzy inference system (ANFIS) model for prediction the springback angle of the SPCC material after U-bending. Three parameters were selected as the main factors of affecting the springback after bending, including the die clearance, the punch radius, and the die radius. The training data were obtained from results of U-bending experiment. The training data with four different membership functions triangular, trapezoidal, bell, and Gaussian functions were employed in the ANFIS to construct a predictive model for the springback of the U-bending. After the comparison of the predicted value with the checking data, the results show that the triangular membership function has the best accuracy, which make it the best function to predict the springback angle of sheet metals after U-bending.

关键词： U-bending springback adaptive-network-based fuzzy inference system sheet metal forming

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Dislocation substructures evolution and an adaptive-network- based fuzzy inference system model for constitutive behavior of a Ni-based superalloy during hot deformation

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JOURNAL OF ALLOYS AND COMPOUNDS 2017年 708卷 938-946页

作者： Chen, Dong-Dong Lin, Y. C. Zhou, Ying Chen, Ming-Song Wen, Dong-Xu Cent S Univ Sch Mech & Elect Engn Changsha 410083 Hunan Peoples R China State Key Lab High Performance Complex Mfg Changsha 410083 Hunan Peoples R China Cent S Univ Light Alloy Res Inst Changsha 410083 Hunan Peoples R China

The isothermal compressive tests with strain rates of (0.001-1) s(-1) and deformation temperatures of (920-1040) degrees C were conducted to study the high-temperature deformation behavior of a Ni-based superalloy. The experimental results show that the true stress decreases with increasing deformation temperature or decreasing strain rate. The effects of deformation temperature and strain rate on dislocation substructures are significant. based on the experimental data, an adaptive-network-based fuzzy inference system (ANFIS) model is constructed for describing the high-temperature deformation behavior of the studied Ni-based superalloy. The inputs of the constructed ANFIS model are deformation parameters (deformation temperature, strain rate and true strain), while the output is true stress. The optimal numbers and types of membership function for the above three input variables are 5-4-5 and 'triangle-shape, triangle-shape, bell-shape', respectively. Comparing the experimental and predicted results, it is found that the constructed ANFIS model can be used to accurately predict the high-temperature deformation behavior of the studied Ni-based superalloy. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Dislocation substructures adaptive-network-based fuzzy inference system Hot deformation Ni-based superalloy Constitutive model

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Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2002年第2期24卷 149-165页

作者： Belal, SY Taktak, AFG Nevill, AJ Spencer, SA Roden, D Bevan, S Keele Univ Ctr Sci & Technol Med Dept Biomed Engn & Med Phys Stoke On Trent ST4 7QB Staffs England N Staffordshire Hosp NHS Trust Clin Technol Dept Stoke On Trent ST4 6QG Staffs England Women & Childrens Div Matern Unit Stoke On Trent ST4 6QG Staffs England

Despite the fact that pulse oximetry has become an essential technology in respiratory monitoring of neonates and paediatric patients, it is still fraught with artefacts causing false alarms resulting from patient or probe movement. As the shape of the plethysmogram has always been considered as a useful visual indicator for determining the reliability of SaO(2) numerical readings, automation of this observation might benefit health care providers at the bedside. We observed that the systolic upstroke time (t(1)), the diastolic time (t(2)) and heart rate (HR) extracted from the plethysmogram pulse constitute features, which can be used for detecting normal and distorted plethysmogram pulses. We developed a technique for classifying plethysmogram pulses into two categories: valid and artefact via implementations of fuzzy inference systems (FIS), which were tuned using an adaptive-network-based fuzzy inference system (ANFIS) and receiver operating characteristics (ROC) curves analysis. Features extracted from a total of 22,497 pulse waveforms obtained from 13 patients were used to systematically optimise the FIS. A further 2843 waveforms obtained from another eight patients were used for testing the system, and visually classified into 1635 (58%) valid and 1208 (42%) distorted segments. For the optimum system, the area under the ROC curve was 0.92. The system was able to classify 1418 (87%) valid segments and 897 (74%) distorted segments correctly. The calculations of the system's performance showed 87% sensitivity, 81% accuracy and 74% specificity. In comparison with the 95% confidence interval (CI) thresholding method, the fuzzy system showed higher specificity (P = 0.008, P < 0.01), and no significant difference was found between the two methods in terms of sensitivity (P = 0.720, P > 0.05) and accuracy (P = 0.053, P > 0.05). We therefore conclude that the algorithm used in this system has some potential in detecting valid and distorted plethysmogram pulse. However

关键词： adaptive-network-based fuzzy inference system artefact detection oxygen saturation false alarms plethysmogram pulse oximetry neonates paediatrics

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Prediction of cyclosporine A blood levels:: an application of the adaptive-network-based fuzzy inference system (ANFIS) in assisting drug therapy

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EUROPEAN JOURNAL OF CLINICAL PHARMACOLOGY 2008年第8期64卷 807-814页

作者： Goeren, Sezer Karahoca, Adem Onat, Filiz Y. Goeren, M. Zafer Marmara Univ Sch Med Dept Pharmacol & Clin Pharmacol TR-34668 Istanbul Turkey Bahcesehir Univ Fac Engn Dept Comp Engn Istanbul Turkey

Objective Therapeutic drug monitoring (TDM) is a procedure in which the levels of drugs are assayed in various body fluids with the aim of individualizing the dose of critical drugs, such as cyclosporine A. Cyclosporine A assays are performed in blood. Methods We proposed the use of the Takagi and Sugeno-type "adaptive-network-based fuzzy inference system" (ANFIS) to predict the concentration of cyclosporine A in blood samples taken from renal transplantation patients. We implemented the ANFIS model using TDM data collected from 138 patients and 20 input parameters. Input parameters for the model consisted of concurrent use of drugs, blood levels, sampling time, age, gender, and dosing intervals. Results fuzzy modeling produced eight rules. The developed ANFIS model exhibited a root mean square error (RMSE) of 0.045 with respect to the training data and an error of 0.057 with respect to the checking data in the MATLAB environment. Conclusions ANFIS can effectively assist physicians in choosing best therapeutic drug dose in the clinical setting.

关键词： adaptive-network-based fuzzy inference system cyclosporine A data mining fuzzy modeling therapeutic drug monitoring

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APPLICATION OF AN adaptive-network-based fuzzy inference system FOR THE OPTIMAL DESIGN OF A CHINESE BRAILLE DISPLAY

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2005年第1期17卷 50-60页

作者： Yeh, Fung-Huei Tsay, Huoy-Shyi Liang, Shih-Hao Tamkang Univ Dept Mech & Electromech Engn Tamsui 25137 Taipei Hsien Taiwan

This paper develops a forward-inverse prediction technique of an adaptive-network-based fuzzy inference system (ANFIS) to optimize the design of a relay-actuated Chinese Braille display (CBD) and to reduce the operation temperature. The key element in CBD, electromagnetic actuator assembled by relay, level, and plastic pin, must operate in lower temperature in order to have enough functional force exerted by relay to lift up Braille dots (pin-heads). However, electromagnetic actuators are often destroyed by thermal damage and the thrust force sensed from Braille dots is gradually reduced by the raising of operation temperature. Two design parameters that have the major influence on the thrust force and maximum operation temperature, including supply voltage and coil resistance of relay, were first analyzed by the thermal and electromagnetic numerical forward models to establish the basic databases for ANFIS. Using these databases, the optimal design of CBD can be forward-inversely predicted by ANFIS. As a verification of this forward-inverse mode, the forward predicted maximum operation temperature and the design thrust force inversely predicting the optimal supply voltage by ANFIS were compared with experimental data. The comparison indicates that the maximum operation temperature and the thrust force of both natural and force convection models achieved satisfactory accuracy. When two bell-shape membership functions were adopted, the best accuracy of the forward predicted maximum temperature of ANFIS reached is as high as 99.71% for natural convection model, and the best accuracy of the design thrust force for the inversely predicted supply voltage of ANFIS reached is also as high as 95.50% for force convection model. This investigation proves that the forward-inverse prediction mode developed for ANFIS with thermal and electromagnetic analyses can supply a useful soft computing technique for the optimal design of CBD.

关键词： Chinese Braille display adaptive-network-based fuzzy inference system Forward-Inverse prediction

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Machine Learning-Driven Approach for Reducing Tool Wear in Die-Sinking Electrical Discharge Machining

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-21页

作者： Cogun, Can Ayli, Ece Cankaya Univ Mechatron Engn Dept Ankara Turkiye Cankaya Univ Mech Engn Dept Ankara Turkiye

This study examines the use of machine learning (ML) techniques to optimize the basic machining parameters and protrusion dimensions that affect tool shape degeneration in die-sinking electric discharge machining (EDM). The primary objective is to decrease errors and enhance prediction and optimization effectiveness. This study introduces a completely novel tool geometry model aimed at minimizing tool shape degeneration, which, to our knowledge, has not been previously documented in the literature. Additionally, this research represents the first instance of employing ML techniques to generate data for addressing this specific type of problem, further advancing the field of die-sinking EDM. The pivotal machining parameters include discharge current, pulse time and machining depth. Three ML approaches are implemented in this investigation: Artificial Neural network (ANN), adaptive-network-based fuzzy inference system (ANFIS), and Support Vector Machine (SVM). In comparison with experimental outcomes, the ANN technique exhibited superior predictive ability with an coefficient of determination (R2) of 0.99985 and an Mean Relative Error (MRE) of 0.854%. Four distinct EDM machining scenarios are presented and machining parameters and protrusion dimensions are optimized using the ANN technique to decrease tool shape degeneration. Optimizing the machining parameters and diagonal dimensions of the protrusion substantially reduced tool shape degeneration. This research demonstrates the effectiveness of ANN in optimizing machining parameters and improving tool performance in die-sinking EDM. A significant reduction in total wear area of 66.7% was achieved with a considerably lower time cost through the optimized ANN network. While the study demonstrates promising results, its reliance on specific datasets for training may limit the generalizability of the model to broader machining scenarios.

关键词： Electric discharge machining Tool wear Tool shape degeneration Machine learning Artificial neural network adaptive-network-based fuzzy inference system Support vector machine

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Hybridization of Differential Evolution and adaptive-network-based fuzzy inference systemin Estimation of Compression Coefficient of Plastic Clay Soil

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 149-166页

作者： Manh Duc Nguyen Ha NguyenHai Nadhir Al-Ansari MahdisAmiri Hai-Bang Ly Indra Prakash Binh Thai Pham University of Transport and Communications Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran University of Transport Technology Hanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the *** this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of adaptive-network-based fuzzy inference system(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil *** of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for *** showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.

关键词： Compression coefficient differential evolution adaptive-network-based fuzzy inference system machine learning vietnam

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Optimal inverse magnetorheological damper modeling using shuffled frog-leaping algorithm-based adaptive neuro-fuzzy inference system approach

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ADVANCES IN MECHANICAL ENGINEERING 2016年第8期8卷 1687814016662770-1687814016662770页

作者： Lin, Xiufang Chen, Shumei Fuzhou Univ Coll Mech Engn Fuzhou 350116 Peoples R China Fujian Agr & Forestry Univ Dept Informat & Mechatron Engn Jianshan Coll Fuzhou Peoples R China

Magnetorheological dampers have become prominent semi-active control devices for vibration mitigation of structures which are subjected to severe loads. However, the damping force cannot be controlled directly due to the inherent nonlinear characteristics of the magnetorheological dampers. Therefore, for fully exploiting the capabilities of the magnetorheological dampers, one of the challenging aspects is to develop an accurate inverse model which can appropriately predict the input voltage to control the damping force. In this article, a hybrid modeling strategy combining shuffled frogleaping algorithm and adaptive-network-based fuzzy inference system is proposed to model the inverse dynamic characteristics of the magnetorheological dampers for improving the modeling accuracy. The shuffled frog-leaping algorithm is employed to optimize the premise parameters of the adaptive-network-based fuzzy inference system while the consequent parameters are tuned by a least square estimation method, here known as shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach. To evaluate the effectiveness of the proposed approach, the inverse modeling results based on the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach are compared with those based on the adaptive-network-based fuzzy inference system and genetic algorithm-based adaptive-network-based fuzzy inference system approaches. Analysis of variance test is carried out to statistically compare the performance of the proposed methods and the results demonstrate that the shuffled frogleaping algorithm-based adaptive-network-based fuzzy inference system strategy outperforms the other two methods in terms of modeling (training) accuracy and checking accuracy.

关键词： Magnetorheological damper inverse model adaptive-network-based fuzzy inference system shuffled frog-leaping algorithm genetic algorithm

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PAPR Reduction in OFDM Signals: An adaptive-network-based fuzzy inference Approach

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WIRELESS PERSONAL COMMUNICATIONS 2017年第2期92卷 587-601页

作者： Mishra, Amit Zaheeruddin Jaypee Univ Engn & Technol Dept Elect & Commun Engn Guna MP India Jamia Millia Islamia Dept Elect Engn New Delhi India

In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based scheme is analyzed and proposed for reducing the peak-to-average power ratio (PAPR) in multicarrier signals under additive white Gaussian noise and multipath fading (Raleigh) channel environment. This scheme involves training of ANFIS structure in time domain using Orthogonal Frequency Division Multiplexing signals with low PAPR, such as those obtained by approximate gradient project-null subcarrier switching (AGP-NCS) method. Once the ANFIS module is trained, the proposed scheme approximately offers similar reduction in PAPR as compared to AGP-NCS method, with significantly less convergence time and computational complexity. he results show that proposed approach is not only less complex but also maintains the data rate and bit error rate performance compared with other conventional schemes.

关键词： adaptive-network-based fuzzy inference system Peak-to-average power ratio Approximate gradient project-null subcarrier switching Bit error rate

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