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Prognostication of lignocellulosic biomass pyrolysis behavior using ANFIS model tuned by PSO algorithm

FUEL 2019年 253卷 189-198页

作者： Aghbashlo, Mortaza Tabatabaei, Meisam Nadian, Mohammad Hossein Davoodnia, Vandad Soltanian, Salman Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran Univ Teknol MARA Fac Plantat & Agrotechnol Shah Alam 40450 Selangor Malaysia Biofuel Res Team BRTeam Karaj Iran AREEO ABRII Microbial Biotechnol Dept Karaj Iran Inst Res Fundamental Sci IPM Brain Engn Res Ctr POB 19395-5531 Tehran Iran

In-depth knowledge on pyrolysis behavior of lignocellulosic biomass is pivotal for efficient design, optimization, and control of thermochemical biofuel production processes. Experimental thermogravimetric analysis (TGA) is usually employed to peruse the pyrolysis kinetics of biomass samples. In addition to that, the main constituents of biomass (i.e., cellulose, hemicellulose, lignin) as well as the process heating rate can excellently reflect its pyrolysis characteristics through modeling techniques. However, the application of statistical and phenomenological models for extremely complex and highly nonlinear phenomena like lignocellulose pyrolysis is challenging. To address this challenge, adaptive network-based fuzzy inference system (ANFIS) was consolidated with particle swarm optimization (PSO) algorithm to prognosticate the kinetic constants of lignocellulose pyrolysis. More specifically, the PSO algorithm was applied to tune membership function parameters of the ANFIS model. Three ANFIS-PSO topologies were designed and trained to estimate the kinetic constants of lignocellulose pyrolysis, i.e., energy of activation, pre-exponential coefficient, and order of reaction. The input variables of the developed models were biomass main constituents and the process heating rate. The developed models could predict the kinetic constants of lignocellulosic biomass pyrolysis with an R-2 > 0.970, an MAPE < 3.270%, and an RMSE < 5.006. The pyrolysis behaviors of three different biomass feedstocks (unseen data to the developed models) were adequately prognosticated with an R-2 > 0.91 using the developed models, further confirming their fidelity. Overall, the lignocellulose pyrolysis behavior could be reliably and accurately estimated using the trained ANFIS-PSO approaches as an alternative to the TGA measurements. In order to make practical use of the trained models, a handy freely-accessible software platform was designed using the selected ANFIS-PSO models for approximati

关键词： Adaptive network-based fuzzy inference system Lignocellulosic biomass particle swarm optimization algorithm Pyrolysis kinetics Thermogravimetric analysis

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A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2063-2071页

作者： Hajizadeh, Ehsan Mahootchi, Masoud Esfahanipour, Akbar Massahi, Mandi Kh Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

An accurate estimation of exchange rate return volatility is an important step in financial decision making problems. The main goal of this study is to enhance the ability of GARCH-type family models in forecasting the Euro/Dollar exchange rate volatility. For this purpose, a new neural-network-based hybrid model is developed in which a predefined number of simulated data series generated by the calibrated GARCH-type model along with other explanatory variables is used as input variables. The optimum number of these data series and other parameters of the network are tuned by an efficient particle swarm optimization algorithm. Using two datasets of real Euro/Dollar rates, how the proposed hybrid model could reasonably enhance the results of GARCH-type models and the traditional neural network in terms of different performance measures is demonstrated. We also illustrate how the respective simulated data series as the input variable to the network could contribute to improve the accuracy of volatility forecasting.

关键词： Exchange rate volatility GARCH models Artificial neural networks particle swarm optimization algorithm Simulated data series

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Solving optimization of a Mine Gas Sensor Layout Based on a Hybrid GA-DBPSO algorithm

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IEEE SENSORS JOURNAL 2019年第15期19卷 6400-6409页

作者： Zhao, Dan Zhang, Hui Pan, Jingtao Liaoning Tech Univ Coll Safety Sci & Engn Fuxin 123000 Peoples R China Liaoning Tech Univ Minist Educ Key Lab Mine Thermodynam Disasters & Control Huludao 125105 Peoples R China Shenyang Res Inst China Coal Technol Engn Grp Shenyang 110016 Liaoning Peoples R China

To expand the monitoring range of the coal mine gas monitoring subsystem and achieve the timely early-warning of the local gas transfinite accident, the covering model of sensor was established. It can realize the function of using the least number of gas sensors to monitor the change of gas concentration in the whole mine. The objective function and constraint conditions of the established model were determined. A hybrid GA-DBPSO algorithm combining the genetic algorithm with the discrete binary particle swarm optimization algorithm was proposed to solve the gas sensor location set covering model. This research result was applied to investigate the gas sensor layout of Baoxin Coal Mine in China, and gas sensor layout schemes were obtained under the condition of different gas sensor shortest alarm time. The relationship between the shortest alarm time and the number of additional gas sensor was given, which can provide guidance and reference for the enterprises managers to make decisions on layout scheme of gas sensors.

关键词： Gas sensor layout optimization particle swarm optimization algorithm discrete combination optimization systems modeling

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Low-level control technology of micro autonomous underwater vehicle based on intelligent computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8569-S8580页

作者： Zhang, Lanyong Zhang, Lei Liu, Sheng Zhou, Jiajia Papavassiliou, Christos Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Imperial Coll London London SW7 2AZ England State Key Lab Millimeter Waves Nanjing 210096 Jiangsu Peoples R China

Based on the modeling of a micro autonomous underwater vehicle, an improved control structure and underlying control method are proposed for some parameters such as the automatic orientation, automatic depth, height, and speed of the micro autonomous underwater vehicle. A cascade double closed-loop control structure is proposed to control the horizontal plane by controlling properties such as the automatic depth, height, positioning, the response speed and adjustment precision of the control are improved. The parameters of the proportional-integral-derivative (PID) control method can be optimized by using particle swarm optimization (PSO), and the fuzzy controller is designed to compare with the PID control of the autonomous underwater vehicles. Compared with the traditional PID control, the control effect of PSO-PID controller is stronger than that of the tranditional PID controller. Due to the uncertainty of the micro autonomous underwater vehicle mathematical model, the position control of PID controller is weaker than the fuzzy controller. The simulation results show that the proposed method has fast dynamic response and acceptable robustness.

关键词： Micro autonomous underwater vehicle particle swarm optimization algorithm Fuzzy control Intelligent computing Low-level control technology

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Research on a hybrid LSSVM intelligent algorithm in short term load forecasting

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8271-S8278页

作者： Xiong, Junhua Wang, Tingling Li, Ruisheng North China Univ Water Resources & Elect Power Sch Elect Power Zhengzhou 450045 Henan Peoples R China State Grid Xuji Grp Corp Res Ctr Xuchang 461001 Peoples R China

For enhancing the prediction accuracy of power load forecasting, a support vector machine (SVM) prediction model based on wavelet transform and the mutant fruit fly parameter optimization intelligent algorithm (WT-MFOA-LSSVM) was presented. The load data were pretreated by wavelet transform, and the load curves were decomposed into different scales, in order to strengthen the regularity and randomness of historical data. Aiming at overcoming the problems of low convergence precision and easily relapsing into local extreme in basic fruit fly optimization algorithm (FOA), judge whether the intelligent algorithm was trapped in local extreme from the fitness variance of the population and the current optimal. Then, it was conducted by optimal individual perturbation and Gauss mutation operation and the mutant fruit flies were second times optimized, which made the accuracy of prediction model be obviously enhanced. The next few days of historical load data of a certain area of Henan Province, China, in 2015 were predicted by using WT-MFOA-LSSVM, and then the prediction results were compared with the results predicted by the SVM model and by the SVM model based on particle swarm optimization model. The results showed that WT-MFOA-LSSVM had high precision in short term load forecasting, and it had a very good practical significance.

关键词： Support vector machine particle swarm optimization algorithm Mutant fruit fly algorithm Wavelet transform Intelligent algorithm Short term load forecasting

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Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regression

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第5期30卷 2217-2230页

作者： Zhang, Feng Zhou, Taotao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Hubei Peoples R China

Magnetic field assisted laser welding (LW-MF) shows great potential in the jointing of large structures. The quality of the welding joint in LW-MF largely depends on the selection of process parameters. In this study, an integrated process parameter optimization framework is developed for magnetic field assisted laser welding. Firstly, Taguchi method is selected to generate sample points and the LW-MF experiments are carried out to obtain the bead geometrical characteristics. Secondly, a sample-sorted SVR (SS-SVR) metamodeling approach is developed to make full use of the already-acquired prediction error information for fitting the relationships between multiple process parameters and the bead geometrical characteristics. A detailed comparison between the developed SS-SVR metamodeling approach and existing SVR metamodeling approach for prediction accuracy is performed. Then, the particle swarm optimization is used to solve the process parameters optimization problem, in which the objective function values are predicted by the developed SS-SVR metamodel. Finally, verification experiment is conducted to verify the reliability of the obtained optimal process parameters. Results illustrate that the proposed integrated process parameter optimization framework is effective for obtaining the optimal process parameters and can be used in LW-MF for practical production.

关键词： Process parameters Magnetic field Laser welding Support vector regression particle swarm optimization algorithm

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optimization design of general Type-2 fuzzy logic controllers for an uncertain Power-line inspection robot

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第2期37卷 2203-2214页

作者： Zhao, Tao Wu, Qing Li, Shengchuan Guo, Rui Dian, Songyi Jia, Hairui Sichuan Univ Coll Elect Engn & Informat Technol Chengdu Sichuan Peoples R China State Grid Liaoning Elect Power Co Ltd Elect Power Res Inst Shenyang Liaoning Peoples R China State Grid Shandong Elect Power Co Jinan Shandong Peoples R China Zhejiang Univ Finance & Econ China Acad Finance Res Hangzhou 310018 Zhejiang Peoples R China

In this paper, a general type-2 fuzzy logic controller (GT2FLC), which is optimized by the particle swarm optimization (PSO) algorithm, is applied to a power-line inspection (PLI) robot. The information fusion is used to design the GT2FLC to avoid the rule explosion. The proposed controller has the ability to deal with uncertainties when the PLI robot works on the insulated access cable. In order to compare the performance of the proposed controller with that of other controllers, the type-1 fuzzy logic controller (T1FLC) and the interval type-2 fuzzy logic controller (IT2FLC) are both optimized by the PSO to adjust the PLI robot. To show the ability of different controllers to deal with uncertainties, external disturbances and parameter perturbations are added to the PLI robot. According to simulations, the performance of the proposed controller is better than that of other controllers, and the proposed controller has better ability to deal with uncertainties.

关键词： Power-line inspection robot particle swarm optimization algorithm general type-2 fuzzy logic controller

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ADRC Based on Artificial Neural Network for a Six-rotor UAV

ADRC Based on Artificial Neural Network for a Six-rotor UAV

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第40届中国控制会议

作者： Lin Xi Yunfeng Shao Suli Zou Zhongjing Ma Beijing Institute of Technology Lvliang power supply company of Shanxi Electric Power Company

An on-line intelligent optimization method based on an artificial neural network is proposed for the parameter adjustment of the active disturbance rejection controller. And a cascaded ADRC controller including the artificial neural network attitude ADRC is investigated for trajectory tracking of the six-rotor UAV. First, establish the kinematics and dynamics model of the six-rotor, and design a cascaded active disturbance rejection controller for the six-rotor to deal with the non-linear disturbance problem in flight. Secondly, an artificial neural network is designed to optimize the parameters of the attitude ADRC controller on-line, and the particle swarm algorithm is used to set the initial value of the artificial neural network. Finally, the simulation results demonstrated that ADRC based on the artificial neural network can effectively resist the disturbances and enhance the robustness of the attitude controller and the cascade ADRC controller based on the artificial neural network can track the reference trajectory quickly and accurately.

关键词： active disturbance rejection control particle swarm optimization algorithm artificial neural network

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Optimal Scheduling Method for Cogeneration System Based on Heat Storage Device

Optimal Scheduling Method for Cogeneration System Based on H...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang Xu Pang Xinfu Zhao Wenjing Liu Li Zhang Yifan Mou Zurong Shenyang Inst Engn Key Lab Energy Saving & Controlling Power Syst Li Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Elect Power Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Automat Shenyang 110136 Peoples R China State Grid Liaocheng Elect Power Supply Co Liaocheng 252000 Shandong Peoples R China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556

In the north of China, the problem of wind abandonment is very serious. In order to improve the ability of wind power consumption, a cogeneration system with an electric boiler is proposed. First, the mathematical model of the minimum power generation cost of the traditional unit and the optimized model are established. Second, the model is solved by using the particle swarm optimization algorithm. In addition, a power structure of combined heat and power system (CHP) is constructed for simulation experiments. Through experimental analysis, proving the feasibility of the model. Finally, Simulation results show that making the electric boiler work in the period of wind abandonment can effectively alleviate the wind abandonment phenomenon. It also can provide more space for the wind power and enhance the wind power consumption.

关键词： Combined heat and power generation Wind abandonment Electric boiler Wind power particle swarm optimization algorithm

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Research on Tennis Recognition Technology Based on Computer Vision

Research on Tennis Recognition Technology Based on Computer ...

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2021 International Conference On Electronics, Circuits And Information Engineering(2021年电子、电路与信息工程国际会议)

作者： Zhang Lu Information Engineering University Luoyang Henan China 471003

ISBN: (纸本)9781665448109

Visual attention mechanism is one of the important means for human beings to perceive the external *** mathematical models to introduce visual attention mechanism into computer vision to simulate human visual perception system is a hot research topic in the field of computer *** research of visual attention model is not only helpful for human beings to better explore the working mechanism of human visual attention,but also has very important significance for solving large-scale data screening and improving image processing efficiency,which has important application value in moving object detection,machine vision,image information matching,image compression and other *** visual attention model is used to preprocess the video sequence,and the region of interest is found as the candidate region for target *** the color and shape matching algorithm is used to match the candidate *** on tennis video show that the algorithm can recognize the target well when the color and shape of the target are relatively single and the significance in the background is high.

关键词： Visual Attention Vector Processor Moving Target Detection particle swarm optimization algorithm Tennis Recognition Model

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