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adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm

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EXPERT systemS WITH APPLICATIONS 2009年第2期36卷 3216-3222页

作者： Ho, Wen-Hsien Tsai, Jinn-Tsong Lin, Bor-Tsuen Chou, Jyh-Horng Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan Kaohsiung Med Univ Dept Med Informat Management Kaohsiung 807 Taiwan Natl Pingtung Univ Educ Dept Comp Sci Pingtung 900 Taiwan

In this paper, an adaptive network-based fuzzy inference system (ANFIS) with the genetic learning algorithm is used to predict the workpiece surface roughness for the end milling process. The hybrid Taguchi-genetic learning algorithm (HTGLA) is applied in the ANFIS to determine the most suitable membership functions and to simultaneously find the optimal premise and consequent parameters by directly minimizing the root-mean-squared-error performance criterion. Experimental results show that the HTGLA-based ANFIS approach outperforms the ANFIS methods given in the Matlab toolbox and reported recently in the literature in terms of prediction accuracy. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Surface roughness End milling process adaptive network-based fuzzy inference system Hybrid Taguchi-genetic learning algorithm

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Thermal error modeling of a machining center using grey system theory and adaptive network-based fuzzy inference system

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JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL systemS MACHINE ELEMENTS AND MANUFACTURING 2006年第4期49卷 1179-1187页

作者： Wang, Kun-Chieh Tseng, Pai-Chung Lin, Kuo-Ming Ling Tung Univ Dept Technol Prod Design Taichung 40852 Taiwan Natl Chung Hsing Univ Dept Mech Engn Changhua Changhua County Taiwan

Thermal effect on machine tools is a well-recognized problem in an environment of increasing demand for product quality. The performance of a thermal error compensation system typically depends on the accuracy and robustness of the thermal error model. This work presents a novel thermal error model utilizing two mathematic schemes: the grey system theory and the adaptive network-based fuzzy inference system (ANFIS). First, the measured temperature and deformation results are analyzed via the grey system theory to obtain the influence ranking of temperature ascent on thermal drift of spindle. Then, using the highly ranked temperature ascents as inputs for the ANFIS and training these data by the hybrid learning rule, a thermal compensation model is constructed. The grey system theory effectively reduces the number of temperature sensors needed on a machine structure for prediction, and the ANFIS has the advantages of good accuracy and robustness. For testing the performance of proposed ANFIS model, a real-cutting operation test was conducted. Comparison results demonstrate that the modeling schemes of the ANFIS coupled with the grey system theory has good predictive ability.

关键词： grey system theory adaptive network-based fuzzy inference system artificial neural network CNC machine tools thermal error compensation

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A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

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Acta Oceanologica Sinica 2017年第11期36卷 94-105页

作者： ZHANG Zeguo YIN Jianchuan WANG Nini HU Jiangqiang WANG Ning Navigation College Dalian Maritime University Dalian 116026 China Department of Mathematics Dalian Maritime University Dalian 116026 China Marine Engineering College Dalian Maritime University Dalian 116026 China

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selecte

关键词： tidal level prediction harmonious analysis method adaptive network-based fuzzy inference system correlation analysis

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Forward and reverse mapping of multiresponses turning process using adaptive network-based fuzzy inference system and simulated annealing algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第10期232卷 1746-1758页

作者： Teimouri, Reza Amini, Saeid Univ Kashan Dept Mfg Fac Mech Engn Ghotb Ravand St Kashan *** Iran

Input-output relationships of a turning process have been established both in forward as well as reverse directions using adaptive network-based fuzzy inference system. Four input parameters, namely insert type, lubrication strategy, feed rate, and cutting velocity and two outputs, namely cutting force and surface roughness have been considered for the aforementioned mappings. Training and testing of the network in the forward direction were adopted by use of experimental data, which derived from high-speed turning of Monel K500 super alloy. For multiattributes reverse mapping problem, grey relational grade was firstly used to convert the cutting force and surface roughness in a single attribute problem. Then the reverse mapping was performed by the use of simulated annealing algorithm by minimizing the absolute difference between grey relational grade of specified cutting force and surface roughness and adaptive network-based fuzzy inference system model of grey relational grade, which was derived from the forward mapping. The confirmation was performed in eight benchmark tests. Results indicated that the proposed methodology can predict the input-output relationship of high-speed turning process in both forward and backward directions with error goal below 8%. The developed model was further used to find the parametric influence of the process factors on the cutting force and surface roughness.

关键词： Hard turning forward and reverse mapping adaptive network-based fuzzy inference system grey relational grade simulated annealing algorithm

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Forward and reverse mappings of electrical discharge machining process using adaptive network-based fuzzy inference system

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EXPERT systemS WITH APPLICATIONS 2010年第12期37卷 8566-8574页

作者： Maji, Kuntal Pratihar, Dilip Kumar Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

Input-output relationships of an electrical discharge machining process have been established both in forward as well as reverse directions using adaptive network-based fuzzy inference system. Three input parameters, such as peak current, pulse-on-time and pulse-duty-factor, and two outputs, namely material removal rate and surface roughness have been considered for the said mappings. A batch mode of training has been adopted with the help of 1000 data for the developed adaptive network-based fuzzy inference system, which has been designed using linear (say triangular) and non-linear (bell-shaped) membership function distributions of the input variables, separately. The performances of the developed models have been tested for both forward and reverse mappings with the help of some test cases collected through the real experiments. adaptive network-based fuzzy inference system is found to tackle the problems of forward and reverse mappings efficiently. The fuzzy inference system utilizing non-linear membership functions is seen to perform slightly better than that with linear membership functions for the input variables. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Electrical discharge machining Forward mapping Reverse mapping adaptive network-based fuzzy inference system

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Modelling of laser processing cut quality by an adaptive network-based fuzzy inference system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2009年第10期223卷 2369-2381页

作者： Subramonian, Sivarao Brevern, P. El-Tayeb, N. S. M. Vengkatesh, V. C. Univ Tekn Malaysia Melaka Mfg Engn Fac Ayer Keroh 75450 Melaka Malaysia

Real-world problems in precision machining now require intelligent systems that integrate knowledge, techniques, and methodologies. Intelligent systems possess human-like expertise within a specific domain to adapt themselves and to learn to do better in making decisions for an intelligent manufacturing system. An intelligent tool called adaptive network-based fuzzy inference system (ANFIS) was used to model and predict the laser cut quality of a 2.5 mm manganese-molybdenum (Mn-Mo) alloy pressure vessel plate in this article. A 3 kW CO2 laser machine with seven selected design parameters was used to carry out 128 experiments based on 2(k) factorial design with single replication. Because surface roughness (Ra) was the response parameter, it was targeted to be < 15 mu m to meet the requirement and benchmark of the pressure vessel manufacturer who sponsored this project. The DIN 2310-5 German laser cutting of metallic materials standard and procedure was referred to for evaluating surface roughness, where experimentally obtained results were used for Ra predictive modelling. Predictions of non-linear laser processing by ANFIS were found to be extremely promising in supplying the desired output, where Ra was predicted to an excellent degree of accuracy, reaching almost 70 per cent with the experimental pure error below 30 per cent.

关键词： adaptive network-based fuzzy inference system laser machining surface roughness cut quality hybrid modelling

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Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system

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EXPERT systemS WITH APPLICATIONS 2010年第2期37卷 1185-1191页

作者： Chen, Miao-Sheng Ying, Li-Chih Pan, Mei-Chiu Cent Taiwan Univ Sci & Technol Dept Mkt Management Taichung 40601 Taiwan Nanhua Univ Dept Business Adm Chiayi 622 Taiwan

Since accurate forecasting of tourist arrivals is very important for planning for potential tourism demand and improving the tourism infrastructure, various tourist arrivals forecasting methods have been developed. The purpose of this study is to apply the adaptive network-based fuzzy inference system (ANFIS) model to forecast the tourist arrivals to Taiwan and demonstrate the forecasting performance of this model based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the fuzzy time series model. grey forecasting model and Markov residual modified model Thus, the ANFIS model is a promising alternative for forecasting the tourist arrivals. We also use the ANFIS model to forecast the monthly tourist arrivals to Taiwan from Japan, Hong Kong and Macao. and the United States (C) 2009 Elsevier Ltd All rights reserved

关键词： adaptive network-based fuzzy inference system Tourist arrivals

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Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2008年第2期43卷 141-149页

作者： Akdemir, Bayram Kara, Sadik Polat, Kemal Guven, Ayegul Gunes, Salih Fatih Univ Dept Elect & Elect Engn TR-34500 Istanbul Turkey Selcuk Univ Dept Elect & Elect Engn TR-42075 Konya Turkey Erciyes Univ Dept Biomed Engn TR-38039 Kayseri Turkey

Objective: This paper presents a new method based on combining principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to diagnose the optic nerve disease from visual-evoked potential (VEP) signals. The aim of this study is to improve the classification accuracy of ANFIS classifier on diagnosis of optic nerve disease from VEP signals. With this aim, a new classifier ensemble based on ANFIS and PCA is proposed. Methods and material: The VEP signals dataset include 61 healthy subjects and 68 patients suffered from optic nerve disease. First of all, the dimension of VEP signals dataset with 63 features has been reduced to 4 features using PCA. After applying PCA, ANFIS trained using three different training-testing datasets randomly with 50-50% training-testing partition. Results: The obtained classification results from ANFIS trained separately with three different training-testing datasets are 96.87%, 98.43%, and 98.43%, respectively. And then the results of ANFIS trained with three different training-testing datasets randomly with 50-50% training-testing partition have been combined with three different ways including weighted arithmetical mean that proposed firstly by us, arithmetical mean, and geometrical mean. The classification results of ANFIS combined with three different ways are 98.43%, 100%, and 100%, respectively. Also, ensemble ANFIS has been compared with ANN ensemble. ANN ensemble obtained 98.43%, 100%, and 100% prediction accuracy with three different ways including arithmetical mean, geometrical mean and weighted arithmetical mean. Conclusion: These results have shown that the proposed classifier ensemble approach based on ANFIS trained with different train-test datasets and PCA has produced very promising results in the diagnosis of optic nerve disease from VEP signals. (C) 2008 Published by Elsevier B.V.

关键词： visual-evoked potential signals optic nerve disease adaptive network-based fuzzy inference system principal component analysis classifier ensemble weighted arithmetical mean

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Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2008年第8期21卷 1189-1195页

作者： Wu, Guan-De Lo, Shang-Lien Natl Taiwan Univ Grad Inst Environm Engn Res Ctr Environm Pollut Prevent & Control Technol Taipei 106 Taiwan

Coagulation is an important component of water treatment. Determining the optimal coagulant dosage is vital, as insufficient dosage will result in unqualified water quality. Traditionally, jar tests and operators' own experience are used to determine the optimum coagulant dosage. However, jar tests are time-consuming and less adaptive to changes in raw water quality in real time. When an unusual condition occurs, such as a heavy rain, the storm water brings high turbidity to water source, and the treated effluent quality may be inferior to drinking water quality standards, because the conventional operation method can be hardly in time to adjust to the proper dosage. An optimal modeling can be used to overcome these limitations. in this paper, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models were used to model poly aluminum chloride (PAC) dosing of northern Taiwan's surface water. Each of them was built based on 819 sets of process-controlled data. The performance of the models was found to be sufficient. Two simulation tools, ANN and ANFIS. were developed that enabled operators to obtain real-time PAC dosage more easily. The self-predicting model of ANFIS is better than ANN for PAC dosage predictions. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Coagulant dosage Artificial neural networks adaptive network-based fuzzy inference system Water treatment

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Estimating tread width values for stair design appropriated to people's comfort and safety needs with adaptive network-based fuzzy inference system

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2023年第1期21卷 188-205页

作者： Diker, Fadime Arslan, Mesut Erkan, Ilker Suleyman Demirel Univ Fac Architecture TR-32200 Isparta Turkey Suleyman Demirel Univ Fac Architecture Student Scholarship Program Isparta Turkey

In this study, it is aimed to develop an adaptive network-based fuzzy inference system (ANFIS) model that can estimate tread width values based on the physical characteristics of people and suitable for comfort and safety needs. The input values were obtained by measuring the height, step, and shoe sole lengths of the sample group of 200 people. For the tread width value to be used as output value, a prototype stair model in which different step sizes can be experienced was used. The tread width value obtained by using the test data in the developed ANFIS model was compared with the tread width value obtained from the experimental study. It has been concluded that the ANFIS model developed as a result of the comparison can be used as an efficient tool in estimating the value of stair tread width, which can meet people's physical comfort needs.

关键词： Stair design tread width adaptive network-based fuzzy inference system anthropometry decision making

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