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A sensing identification method for shearer cutting state based on modified multi-scale fuzzy entropy and support vector machine

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 78卷 86-101页

作者： Si, Lei Wang, Zhongbin Liu, Xinhua Tan, Chao China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Jiangsu Peoples R China

Accurate identification of shearer cutting state is a prerequisite for achieving safe and efficient production in coal mines. In this paper, a novel state diagnosis method is put forward based on modified multi-scale fuzzy entropy (MMFE) and support vector machine (SVM). On the basis of multi-scale entropy and fuzzy entropy, MMFE is designed to obtain stable and accurate estimation for short time series over a range of scales. Therefore, MMFE is employed to extract the feature information of vibration signals of shearer rocker arm and the complexity of time series can be reasonably embodied through some simulation analysis. Besides, the Fisher score (FS) method is utilized to sort the obtained features according to their importance and the first five features with the most important information are selected as the feature vectors. Subsequently, an improved fruit fly optimization algorithm (IFOA) is presented to optimize the parameters of SVM and the IFOA-SVM based multi-classifier is constructed to fulfill an automatic state identification. The experiment results indicate that the proposed state identification method is outperforming others and can effectively distinguish different cutting states of shearer with different operation conditions.

关键词： Shearer Cutting state identification Multi-scale fuzzy entropy fruit fly optimization algorithm Support vector machine

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A FOA-Optimized RBF algorithm-Based Evaluation Research on E-commerce Websites

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 2835-2851页

作者： Zhang, Xinhe Hu, Xia He, Chao Yu, Chunming Northwest Univ Sch Econ & Management Xian 710127 Shaanxi Peoples R China China Univ Min & Technol Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Management Xuzhou 221116 Jiangsu Peoples R China

By applying the Expert Grading Method and considering the features of e-commerce websites and properties of the indicators, this paper constructs a multi-indicator hierarchical structure for the competitiveness index evaluation of e-commerce website as well as built an indicator system for the evaluation. This system can be used to measure the competitiveness index of such a website and quantify its competitiveness. Then Radial Basis Function (RBF) Neural Network algorithm (NNA) is adopted to evaluate and research the competitiveness indexes of e-commerce websites. Against the problems therein, this paper tries to improve the RBF NNA with fruit fly optimization algorithm (FOA). Through the simulation and contrast of examples, FOA-RBF algorithm obviously works better than RBF NNA in measuring and evaluating the competitiveness indexes of such websites. Therefore, it is verified that the algorithm proposed by this paper is both effective and reliable.

关键词： E-commerce Neural network Radial Basis Function fruit fly optimization algorithm Expert Grading Method

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Prediction of microfibril angle in Dahurian larch wood using visible-near infrared spectroscopy and chemometric techniques

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2019年第4期27卷 270-277页

作者： Li, Ying Via, Brian K. Cheng, Qingzheng Li, Yaoxiang Northeast Forestry Univ Coll Engn & Technol Harbin 150040 Heilongjiang Peoples R China Auburn Univ Forest Prod Dev Ctr SFWS Auburn AL 36849 USA

The microfibril angle of the S2 layer in the secondary cell wall of the tracheid is important for molecular and microscopic properties that influence collapse resistance, longitudinal modulus of elasticity and other lateral properties of conifers at the macroscopic level. This research aimed to investigate the feasibility of using a fruit fly optimization algorithm for visible and near infrared modeling optimization of Dahurian larch wood microfibril angle prediction. Originally, the linear relationship between microfibril angle and their raw spectra and visible and near infrared spectra pretreated by wavelet transform was established. Then, a nonlinear coupled model was built by combining the stepwise regression analysis and generalized regression neural network methods. As a final point, fruit fly optimization algorithm was used for optimizing stepwise regression analysis- generalized regression neural network coupled model. It was found that stepwise regression analysis-generalized regression neural network coupled model coupled model based on the optimization of fruit fly optimization algorithm simplify visible and near infrared spectral data and its prediction results ( r(p)(2) = 0.90, RMSEP = 0.75, mean average percentage error ( MAPE(p)) = 0.05) outperforms original partial least squares model ( r(p)(2) = 0.86, RMSEP = 0.88, MAPE(p) = 0.06). This work demonstrated the feasibility of using improved chemometric techniques for improving the precision of visible and near infrared spectra in the prediction of microfibril angle.

关键词： Visible and near infrared spectroscopy stepwise regression analysis-generalized regression neural network coupled model fruit fly optimization algorithm wavelet transform microfibril angle

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Study on medical image enhancement based on IFOA improved grayscale image adaptive enhancement

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第22期75卷 14367-14379页

作者： Xie, Yuxi He, Yonggui Cheng, Aibin Zhang, Junwei North China Univ Sci & Technol Affiliated Hosp Tangshan 063000 Peoples R China

In allusion to deficiencies of traditional medical image enhancement algorithms such as poor applicability, large calculated amount and manual parameter setting and local optimum problem of FOA algorithm, this paper introduces chaos theory into FOA algorithm for improvement based on good global optimum searching performance of fruit fly optimization algorithm and optimizes normalized incomplete Beta function with IFOA for medical image enhancement. The experimental result shows that the improved FOA algorithm can highlight image features effectively, improve visual effect of images and efficiency, avoid invariability of manual parameter adjustment, configure best parameters of normalized incomplete Beta function automatically while guaranteeing best image quality and achieve adaptive enhancement of medical images.

关键词： Chaos theory fruit fly optimization algorithm Adaptive enhancement Image processing Image quality evaluation

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Mixed chaotic FOA with GRNN to construction of a mutual fund forecasting model

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COGNITIVE SYSTEMS RESEARCH 2018年 52卷 380-386页

作者： Han, Shi-Zhuan Huang, Li-Hui Zhou, Ying-Ying Liu, Zong-Li East China Jiaotong Univ Sch Econ & Management Nanchang Jiangxi Peoples R China Guangdong Univ Foreign Studies Sch Business Guangzhou Guangdong Peoples R China

This article attempts to collect the research data of mutual fund in Taiwan, evaluates the management performance of each fund by using the Data Envelopment Analysis and selects the mutual fund whose technical efficiency value is 1 as the investment goal. Then, this article optimizes the parameter of GRNN by using a variety of intelligence algorithms including AFSA, PSO and relatively new FOA, build the forecasting model of mutual fund net value and analyzes the prediction ability with GRNN model that is not optimized. After the compares of five prediction performance evaluation indicators, the model that optimizes the GRNN by using FOA has the highest prediction accuracy among four prediction models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Artificial intelligence Particle swarm optimization Artificial fish swarm algorithm fruit fly optimization algorithm Mutual fund

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Improved Neural Network Control Approach for a Humanoid Arm

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2019年第10期141卷 101009页

作者： Liu, Xinhua Zhang, Xiaohui Malekian, Reza Sarkodie-Gyan, Th. Li, Zhixiong China Univ Min & Technol Sch Mechatron Engn Xuzhou 211006 Jiangsu Peoples R China Malmo Univ Dept Comp Sci & Media Technol S-20506 Malmo Sweden Univ Texas El Paso Coll Engn 500 West Univ Ave El Paso TX 79968 USA Ocean Univ China Sch Engn Tsingdao 266100 Peoples R China Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia

This study extended the knowledge over the improvement of the control performance for a seven degrees-of-freedom (7DOF) humanoid arm. An improved adaptive Gaussian radius basic function neural network (RBFNN) approach was proposed to ensure the reliability and stability of the humanoid arm control. Considering model uncertainties, the established dynamic model for the humanoid arm was divided into a nominal model and an error model. The error model was approximated by the RBFNN learning to compensate the uncertainties. The contribution of this study mainly concentrates on employing fruit fly optimization algorithm (FOA) to optimize the basic width parameter of the RBFNN, which can enhance the capability of the error approximation speed. Additionally, the output weights of the neural network were adjusted using the Lyapunov stability theory to improve the robustness of the RBFN-based error model. The simulation and experiment results demonstrate that the proposed approach is able to optimize the system state with less tracking errors, regulate the uncertain nonlinear dynamic characteristics, and effectively reduce unexpected interferences.

关键词： adaptive control fruit fly optimization algorithm humanoid arm radial basis function network

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Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase

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BIOSYSTEMS ENGINEERING 2018年 173卷 124-133页

作者： Cross, Amanda J. Rohrer, Gary A. Brown-Brandl, Tami M. Cassady, Joseph P. Keel, Brittney N. South Dakota State Univ Dept Anim Sci Brookings SD 57007 USA ARS USDA US Meat Anim Res Ctr Clay Ctr NE 68933 USA

Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Published by Elsevier Ltd on behalf of IAgrE.

关键词： Pigs Feeding behaviour Artificial neural network fruit fly optimization algorithm

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A new combined braking approach of magnetorheological fluids brake based on fuzzy controller and improved PID controller

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第23期235卷 6644-6658页

作者： Hua, Dezheng Liu, Xinhua Gupta, Munish Kumar Fracz, Pawel Li, Zhixiong China Univ Min & Technol Sch Mechatron Engn Xuzhou Jiangsu Peoples R China Technol & Innovat Res Ctr JiangYan EDZ Taizhou Peoples R China Shandong Univ Sch Mech Engn Jinan Shandong Peoples R China Opole Univ Technol Dept Mfg Engn & Automat Prod Opole Poland Ocean Univ China Sch Engn Qingdao Shandong Peoples R China Yonsei Univ Yonsei Frontier Lab 50 Yonsei Ro Seoul 03722 South Korea

With the aim to improve working performance of magnetorheological fluids (MRF) brake, a combined braking approach is proposed in this paper. A data acquisition system of MRF brake is designed and transfer function is identi?ed for the device. Furthermore, based on the excitation current-extrusion pressure combined braking system, a fuzzy controller is designed to distribute the target torque. Moreover, through integration of fruit fly optimization algorithm (FOA) and particle swarm optimization algorithm (PSOA), an improved proportional-integral-differential (PID) controller is presented to conduct braking mechanism. Finally, the simulations and experiments are carried out. The results show that the combined braking approach with fuzzy controller and improved PID controller has characteristics of quick response and no overshoot, and superior to conventional PID and FOA PID controller.

关键词： Combined braking system magnetorheological fluids brake fuzzy controller PID controller fruit fly optimization algorithm

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BIFFOA: A Novel Binary Improved fruit fly algorithm for Feature Selection

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IEEE ACCESS 2019年 7卷 81177-81194页

作者： Hou, Yun Li, Jingyao Yu, Haihong Li, Zhanshan Natl Educ Minist Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Coll Software Changchun 130012 Jilin Peoples R China

Feature selection is an important method to reduce the number of attributes of high-dimensional data and an essential preprocess work in classification. It eliminates irrelevant, redundant, and noisy features improves the performance of the model and reduces the computational burden. fruit fly optimization algorithm is a newalgorithm proposed in recent years, which imitates the foraging behavior of fruit fly. To the best of our knowledge, it has not been systematically applied to feature selection. This paper uses the fruit fly optimization algorithm as a search strategy and designs a wrapper-based feature selection method, named binary improved fruit fly optimization algorithm (BIFFOA). Besides, four different strategies based on evolutionary population dynamics (EPD) and new mutation operators are employed to enhance the BIFFOA. The extensive experiments on 25 datasets (see Table 1) show that the performance of the BIFFOA is better than several state-of-the-art algorithms.

关键词： Classification evolutionary population dynamics feature selection fruit fly optimization algorithm

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A novel water quality mechanism modeling and eutrophication risk assessment method of lakes and reservoirs

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NONLINEAR DYNAMICS 2019年第2期96卷 1037-1053页

作者： Wang, Xiaoyi Zhou, Yuqin Zhao, Zhiyao Wang, Li Xu, Jiping Yu, Jiabin Beijing Technol & Business Univ Sch Comp & Informat Engn 11-33 Fucheng Rd Beijing 100048 Peoples R China

Water quality mechanism modeling and eutrophication analysis are important technical means for water pollution prevention and control of lake and reservoirs. However, the existing classical water quality mechanism models usually contain unknown parameters with empirical values range, which brings difficulty of predicting water quality changes of specific lakes and reservoirs to meet the accuracy requirements. Furthermore, the most existing water quality prediction methods output single-valued predictions of water quality indicators. These prediction results possess contingencies and uncertainties. Eutrophication analysis based on these results will bring errors, even become completely wrong. Therefore, combining the evolution mechanism of water quality, this paper proposes a fruit fly optimization algorithm (FFOA)-based water quality mechanism modeling method and studies a eutrophication risk assessment method of lakes and reservoirs based on the mechanism model. First, combining observed water quality data, unknown parameters of a water quality mechanism model are estimated by using FFOA. Then, Monte Carlo simulation is employed to predict the evolution of water quality, and the probability distribution functions (PDFs) of water quality indicators at predicted time indexes are obtained to achieve water quality prediction. Finally, to quantitatively assess the eutrophication status, a comprehensive eutrophication status index (CESI) is constructed. The PDF of CESI and the probabilities of being in different eutrophication levels are calculated to achieve water eutrophication risk assessment. The simulation results show that the proposed method can effectively estimate the unknown parameters of the water quality mechanism model, predict water quality evolution and assess the eutrophication risk with better accuracy and rationality by comparing the existing methods.

关键词： Water quality mechanism model fruit fly optimization algorithm Monte Carlo simulation Water quality prediction Eutrophication Risk assessment

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