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Application of adaptive neuro-fuzzy inference system for prediction of internal stability of soils

EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2019年第2期23卷 153-171页

作者： Xue, Xinhua Xiao, Ming Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu Sichuan Peoples R China Penn State Univ Dept Civil & Environm Engn University Pk PA 16802 USA

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the assessment of internal stability of soils under seepage. The training of fuzzy system was performed by a hybrid method of back-propagation (BP) and least mean square algorithm, and the subtractive clustering algorithm was utilised for optimising the number of fuzzy rules. Experimental data on internal stability of soils in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the BP model, the particle swarm optimisation-BP model and the ANFIS model) were compared with the experimental data. The results show that the ANFIS model is a feasible, efficient and accurate tool for predicting the internal stability of soils according to Wan and Fell's criterion.

关键词： internal stability adaptive neuro-fuzzy inference system particle swarm optimisation back propagation subtractive clustering algorithm

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A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design

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INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS 2004年第1期34卷 31-47页

作者： Park, J Han, SH Pohang Univ Sci & Technol Div Mech & Ind Engn Pohang 790784 South Korea

Affective user satisfaction is considered one of the most important factors in designing consumer products. Researchers have attempted to build models explaining the relationship between design variables of consumer products and affective user satisfaction using statistical multivariate analysis techniques such as linear regression and quantification theory. These techniques, however, have limitations due to the inability of capturing non-linearity of human feelings. This paper proposes a fuzzy rule-based approach to building models relating product design variables to affective user satisfaction. Affective user satisfaction such as luxuriousness, balance, and attractiveness were modeled for office chair designs. Regression models were also built on the same data to compare model performance. The results showed that fuzzy rule-based models were better than regression models in terms of prediction performance and the number of variables included in the model. Methods for interpreting the fuzzy rules are discussed for practical applications in designing office chairs.

关键词： affective user satisfaction fuzzy rule-based models subtractive clustering algorithm office chair design

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Neuro-fuzzy based approach for estimation of concrete compressive strength

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COMPUTERS AND CONCRETE 2018年第6期21卷 697-703页

作者： Xue, Xinhua Zhou, Hongwei Sichuan Univ State Key Lab Hydraul & Mt River Engn Coll Water Resource & Hydropower 24 South Sect 1Yihuan Rd Chengdu 610065 Sichuan Peoples R China

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

关键词： concrete compressive strength adaptive neuro-fuzzy inference system least squares method subtractive clustering algorithm

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Research into a RFID Neural Network Localization algorithm

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2016年第5期9卷 295-303页

作者： Jin, Jiangang North China Univ Water Resources & Elect Power Software Technol Vocat Coll Zhengzhou 450045 Peoples R China

The accuracy of indoor positioning algorithm has been the focus of research. In this paper;a particle swarm optimization algorithm based on particle swarm optimization algorithm and K - means algorithm is proposed. In this paper;firstly, the indoor positioning RFID model is constructed, and the positioning equation is constructed, then reduce the clustering algorithm to avoid human interference, through the K means algorithm to form a particle swarm algorithm to initialize the particle swarm algorithm, finally, the particle swarm optimization algorithm is used to train all the parameters of RBF neural network, and then the optimal output model is obtained. Simulation results show that the algorithm can effectively improve the positioning accuracy, reduce energy consumption, and improve the positioning accuracy of 10%.

关键词： RFID Indoor Positioning subtractive clustering algorithm K - means RBF

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An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions

An ANFIS Based Model for Predicting Frost Heaving in Seasona...

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3rd International Conference on Civil Engineering and Transportation (ICCET 2013)

作者： Xiang Yiming Liu Xueyan Ling Guixiang Du Bin Guizhou Commun Planning Survey & Design Inst Co L Guiyang 550022 Peoples R China

ISBN: (纸本)9783038350057

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

关键词： Frost heaving hybrid learning algorithm subtractive clustering algorithm adaptive network-based fuzzy inference system

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An Optimal Design Methodology of Adaptive Neuro-Fuzzy Inference System for Energy Load Forecasting - Hail city case study (Saudi Arabia) 18

An Optimal Design Methodology of Adaptive Neuro-Fuzzy Infere...

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4th International Conference on Engineering and MIS (ICEMIS)

作者： Hussein, Hisham Hail Univ Community Coll Dept Elect Engn Hail Saudi Arabia

ISBN: (纸本)9781450363921

In this paper, it is shown that accurate load forecasts are vital for short, medium and long-term operations. The energy load forecast has its impact on different outcomes and decisions for power generation companies. It also has its influence on electricity market prices. The purpose of this research is to develop an energy load forecasting model to predict future electricity loads for energy load management. The forecasting model is based on a straightforward sequential methodology by implementing subtractive clustering algorithm, fuzzy C-means clustering algorithm and eventually an adaptive Neuro-Fuzzy inference system architecture for generating the best fuzzy inference system using historical energy load data. In addition, the influence of different weather factors on energy loads such as dry-bulb temperature is counted in.

关键词： Adaptive Neuro-Fuzzy inference system Energy load forecast Fuzzy C-means clustering algorithm Fuzzy inference system subtractive clustering algorithm

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A Design of Inverse Control System Based On Multiple Support Vector Machine

A Design of Inverse Control System Based On Multiple Support...

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5th IEEE Conference on Industrial Electronics and Applications

作者： Mao, Xuefei Zhang, Shaode Mao, Xueqin Anhui Univ Technol Dept Elect Engn & Informat Maanshan Anhui Province Peoples R China

ISBN: (纸本)9781424450466

The paper works out an online self-learning control plant for multiple support vector machine inverse control. The multiple support vector machine model applies subtractive clustering algorithm by which the input space is divided into several small local spaces. By means of least squares support vector machine, the sub-models are established. The prediction output of each sub-model is connected by principal components regression method so that identification of the inverse dynamics model of the system is achieved. Combining inverse model of the system as a system controller with the controlled plant, a SVM direct inverse control system is constituted. In order to overcome the influence of inverse model identification error, a SVM direct inverse control system with the PID compensation is designed in the paper. The simulation research proves that the control strategy can provide the system with good tracking performance, resistance to interference and a better robustness.

关键词： multiple support vector machine least squares support vector machine subtractive clustering algorithm principal components regression particle swarm optimization

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Constellation Based Signal Modulation Recognition for MQAM 9

Constellation Based Signal Modulation Recognition for MQAM

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Liu Wang Yubai Li School of Communication & Information Engineering University of Electronic Science and Technology of China

ISBN: (纸本)9781509038237;9781509038220

QAM modulation signal has been widely used in wireless communication system. This paper presents a method of identifying MQAM signals, which is effective even in highorder QAM modulation signals. Owing to different types of modulation signals mapping to different constellations, we propose a constellation-based algorithm. In this paper, we use subtractive clustering algorithm which automatically calculate the center of the constellation, and then according to the number of center points, we can recognize modulation signals. From the simulation results, the method can effective recognize MQAM signals, including: 4 QAM, 16 QAM, 32 QAM, 64 QAM, 128 QAM, 256 QAM. When SNR≥5 d B, 99% of 4 QAM and 16 QAM can be correctly recognized, in addition, for highorder QAM signals 128 QAM and 256 QAM etc., we have a highly recognition rate when SNR≥15 d B.

关键词： automatic modulation recognition MQAM constellation diagram subtractive clustering algorithm

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Structure design for RBF neural network based on improved K-means algorithm 28

Structure design for RBF neural network based on improved K-...

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28th Chinese Control and Decision Conference, CCDC 2016

作者： Yuqing, Sun Junfei, Qiao Honggui, Han College of Electronic Information and Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781467397148

Aiming at the disadvantages of the traditional K-means clustering algorithm, a new algorithm based on density is proposed to remove the noises and outliers in this paper. This algorithm determines whether a point is a noise or not according to the density of the point. Experiments show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the samples. Then the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm, meanwhile the number of cluster centers is gotten. The improved K-means algorithm is taken to optimize the structure of RBF neural network, and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability. © 2016 IEEE.

关键词： Density K-means algorithm RBF neural network subtractive clustering algorithm

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Structure Design for RBF Neural Network Based on Improved K-means algorithm

Structure Design for RBF Neural Network Based on Improved K-...

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第28届中国控制与决策会议

作者： Sun Yuqing Qiao Junfei Han Honggui College of Electronic Information & Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Aiming at the disadvantages of the traditional K-means clustering algorithm,a new algorithm based on density is proposed to remove the noises and outliers in this *** algorithm determines whether a point is a noise or not according to the density of the *** show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the *** the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm,meanwhile the number of cluster centers is *** improved K-means algorithm is taken to optimize the structure of RBF neural network,and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability.

关键词： K-means algorithm Density subtractive clustering algorithm RBF neural network

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