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ANN-BASED DIRECT TORQUE CONTROL SCHEME OF AN IM DRIVE FOR A WIDE RANGE OF SPEED OPERATION

NEURAL NETWORK WORLD 2021年第6期31卷 395-414页

作者： Jeyashanthi, J. BarsanaBanu, J. Sethu Inst Technol Dept Elect & Elect Engn Virudunagar Tamil Nadu India SBM Coll Engn & Technol Dept Elect & Elect Engn Dindugal Tamil Nadu India

Induction motor (IM) drives with direct torque control (DTC) enable fast torque response without the need for complex orientation conversions or inner loop current loop. In the speed estimation responses, however, there is a significant level of torque ripple. The voltage source inverter adds acoustic noise and needs a high sampling frequency since it operates at a high and variable switching frequency. This work describes an ANN-based DTC technique for controlling the speed of an IM drive over a large speed range. To achieve good dynamic performance of induction motor drive, the ANN-based speed controller will replace the speed controller, switching tables, and hysteresis comparators. The neural network was trained using the back-propagation algorithm. The goal of a neural speed controller is to improve the system ability to respond quickly to changes in process variables while also mitigating the impacts of external perturbations. The projected ANN based DTC considerably and simply tracks the reference speed thus improves the efficiency of speed-torque of induction motors with quicker responses for rapid varying of speed reference and torque as that of Electric Vehicles in any uneven roads circumstances. MATLAB/Simulink software is used to evaluate the drive performance for both transient and dynamic operations. The proposed control performance is simulated and compared to a DTC-based traditional PI speed controller. In comparison to PI, the results show that ANN has better and faster effects. The torque ripple gets reduced by 1.5% in ANN (artificial neural network) controller compared to PI controller. The THD (total harmonic distortion) is reduced by 6.38% from PI controller to ANN controller.

关键词： artificial neural networks back propagation algorithm induction motors pid controller torque control total harmonic distortion

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Multilayer feed-forward neural network approach for optimal dispatch of UPFC embedded pool-bilateral electricity market

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第9期25卷 1923-1942页

作者： Geetha, Natarajan Renuga, Perumal Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi 626005 India Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India

The significance and influence of bilateral component on optimal dispatch of mixed pool-bilateral electricity market is the major issue to be examined for trade-off between pool and pre-specified bilateral transactions. This paper proposes single part optimal dispatch model that dispatches pool in coordination with privately negotiated bilateral transactions while minimizing cost of generation, accounting power balance equality constraints and inequality constraints. The critically loaded transmission line is identified, and when Unified Power Flow Controller (UPFC) is placed in it, optimal dispatch solution gets modified because of incorporation of UPFC parameters in the developed formulation. The methodology of multilayer perceptron network is adopted in this work with inputs to the neural network as percentage of bilateral component and status of UPFC for online evaluation of generation cost and incremental cost. The network is trained with back propagation training algorithm, and for IEEE 30 and IEEE 118 bus systems, it is established that Levenberg-Marquardt algorithm outperforms other algorithms in terms of accuracy. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： optimal dispatch mixed pool-bilateral model UPFC back propagation algorithm artificial neural network transaction matrix

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Study of a laboratory-scale froth flotation process using artificial neural networks

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MINERAL PROCESSING AND EXTRACTIVE METALLURGY REVIEW 2008年第2期29卷 130-142页

作者： Kalyani, V. K. Pallavika Chaudhuri, Sanjay Charan, T. Gouri Haldar, D. D. Kamal, K. P. Badhe, Y. P. Tambe, S. S. Kulkarni, B. D. IT Ctr Cent Min Res Inst Dhanbad 826001 Bihar India Cent Fuel Res Inst Dhanbad Bihar India Vinoba Bhave Univ Dept Chem Hazaribagh India NCL Div Chem Engn Pune Maharashtra India

A three-layer feed-forward artificial neural network (ANN) model, trained using the error back propagation algorithm, has been established to simulate the froth flotation process for the beneficiation of coal fines. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used to compute the parameters of the coal flotation process over wide-ranging experimental conditions.

关键词： back propagation algorithm froth flotation laboratory-scale neural network

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Using 2W-PE method based on machine learning to accurately predict field strength distribution in flat-top obstacle environment

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2022年 144卷

作者： Li Anqi Yin Chengyou Zhang Qianqian Natl Univ Def Technol Coll Elect Engn Hefei 230037 Anhui Peoples R China

A new two-way parabolic equation (2W-PE) method is proposed in this paper which uses machine learning to determine boundary condition equations, and accurately predicts the electromagnetic field strength distribution in the environment of media ground and obstacles. On the basis of previous researches, the obstacle area is decomposed according to the principle of domain decomposition, and the processes of solving 2W-PE in each subdomain are briefly explained. Because the direction and strength of the incident waves on the boundaries vary greatly with the propagation environment, it is difficult for us to determine boundary condition equations. The machine learning method is introduced here, and method of moments (MoM) is applied to generate sample data sets, thus, coefficients in boundary condition equations can be trained through the non-end-to-end neural network combined with backpropagation algorithm and gradient descent method. So that no matter how the environment changes, appropriate boundary conditions can be obtained by the well-trained neural network and help improving the accuracy of 2W-PE. Simulation results show that the accuracy of the new 2W-PE method based on machine learning is better than that of 2W-PE with traditional boundary conditions by comparing with the results of MoM, which also reflects the advantages of machine learning in radio wave propagation analysis. Therefore, this paper provides an innovative and reliable method for accurately predicting field distribution in flat-top obstacle environments.

关键词： Two-way parabolic equation Machine learning Artificial neural network back propagation algorithm Surface impedance boundary condition Field strength prediction

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Experimental and neural model analysis of styrene removal from polluted air in a biofilter

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JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY 2009年第7期84卷 941-948页

作者： Rene, Eldon R. Veiga, Maria C. Kennes, Christian Univ A Coruna Chem Engn Lab E-15008 La Coruna Spain

backGROUND: Biofilters are efficient systems for treating malodorous emissions. The mechanism involved during pollutant transfer and subsequent biotransformation within a biofilm is a complex process. The use of artificial neural networks to model the performance of biofilters using easily measurable state variables appears to be an effective alternative to conventional phenomenological modelling. RESULTS: An artificial neural network model was used to predict the extent of styrene removal in a perlite-biofilter inoculated with a mixed microbial culture. After a 43 day biofilter acclimation period, styrene removal experiments were carried out by subjecting the bioreactor to different flow rates (0.15-0.9 m(3) h(-1)) and concentrations (0.5-17.2 g m(-3)), that correspond to inlet loading rates up to 1390 g m(-3) h(-1). During the different phases of continuous biofilter operation, greater than 92% styrene removal was achievable for loading rates up to 250 g m(-3) h(-1). A back propagation neural network algorithm was applied to model and predict the removal efficiency (%) of this process using inlet concentration (g m(-3)) and unit flow (h(-1)) as input variables. The data points were divided into training (115 x 3) and testing set (42 x 3). The most reliable condition for the network was selected by a trial and error approach and by estimating the determination coefficient (R-2) value (0.98) achieved during prediction of the testing set. CONCLUSION: The results showed that a simple neural network based model with a topology of 2-4-1 was able to efficiently predict the styrene removal performance in the biofilter. Through sensitivity analysis, the most influential input parameter affecting styrene removal was ascertained to be the flow rate. (C) 2009 Society of Chemical Industry

关键词： biofiltration back propagation algorithm modelling neural network reactor performance waste gas

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Color image vehicular detection systems with and without fuzzy neural network: A comparison

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2003年第5期26卷 659-670页

作者： Lan, LW Kuo, AY Huang, YC Natl Chiao Tung Univ Inst Traff & Transportat Taipei 100 Taiwan

This paper develops a color image vehicular detection (CIVD) system in which background differencing technique is employed to detect whether a vehicle passes through the detecting points equally spaced out on a pseudo line detector. Two methods (interval search and regression) are tried to determine the optimal crisp threshold values to cope with various lighting conditions. To compare the detection performance with and without incorporating a fuzzy neural network (FNN), a three-layer FNNCIVD system is further designed with trapezoidal membership function and network parameters trained by back propagation algorithm. Under different environments (freeway and urban street) with various lighting conditions (daytime and nighttime), it is found that the detection success rates for interval-search CIVD and regression CIVD are about the same. However, both perform worse than the FNNCIVD system in which about 90% success rates are reported with seven detection points. Compared with the interval-search CIVD system, the FNNCIVD system can increase the success rates at a range of 14% to 22% on the freeway mainline and 18% to 26% on the urban street. It is also found that daytime detection performance is slightly better than nighttime detection. Possible reasons for missed detection and false detection are discussed.

关键词： color image vehicular detection (CIVD) fuzzy neural network (FNN) background differencing technique back propagation algorithm

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A Stable Nonlinear in Parameter Neural Network Controller for a Class of Saturated Nonlinear Systems

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IFAC Proceedings Volumes 2014年第3期47卷 2533-2538页

作者： K. Esfandiari F. Abdollahi H.A. Talebi The Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Concordia University Montreal QC Canada

This paper deals with the tracking problem for a class of uncertain nonlinear systems subjected to actuator saturation constraint. Despite most of proposed control schemes for saturated systems which employed linear in parameter neural networks (LPNNs), in the present work nonlinear in parameter neural network (NLPNN) is introduced to support global approximation property. To compensate the effect of the input saturation constraint an auxiliary system is introduced and the error dynamics are modified based on the auxiliary states. Then, learning rules are achieved based on the back propagation (BP) algorithm and by adding two robustifying terms to the standard BP learning rules the stability of the overall system is ensured via Lyapunov direct method. Finally simulations performed on a “generalized pendulum” nonlinear system to illustrate the effectiveness of the proposed tracking control scheme.

关键词： Adaptive tracking control back propagation algorithm Input saturation Neural networks Nonlinear systems

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Neural Network Approach Based on Agent to Predict Stock Performance

International Journal of Systems and Control

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International Journal of Systems and Control 2008年第3期 173-179页

作者： SHAO Yan-hua1,2,LI Jian-shi1,CHEN Tian-jian 1,TIAN Feng1,WANG Jin-rong1 1.Department of Computer Science and Technology,University of Guizhou,Guiyang 550025,China 2.Department of Economics and Management,Guizhou University for Nationalities,Guiyang 550025,China

Predicting stock performance is a very large and profitable area of study.A large number of studies have been reported in literature with reference to the use of artificial neural network in modeling stock performance in western ***,not much work along the approach to neural network based on agent has been *** thesis focuses on the development and the simulation of a stock market performance model of utilizing a neural network approach base on *** model is easy to understand,and can be easily implemented as a software *** we will discuss the basic concepts behind this type of neural network based on agent,then,we'll get into some of the more application ideas.

关键词： Artificial Neural Network back propagation algorithm Agent Swarm Simulation Toolkit

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Prediction of hot flow stress of CrMoV steel using artificial neural network (ANN)

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ISIJ INTERNATIONAL 2007年第8期47卷 1126-1130页

作者： Bapari, Afshin Najafizadeh, Abbas Moazeny, Mohamamid Isfahan Univ Technol Dept Mat Sci & Engn Esfahan Iran

Hot flow stress of CrMoV steel was extracted using hot compression test. Test was carried out in strain range of 0.1-0.9, with strain rate of 0.1-5 s(-1) and the temperature of 900-1 200 degrees C. Flow stress was predicted by using conventional regression method, i.e., Zenner-Holloman parameter with hyperbolic function. The results showed low accuracy of prediction with a high relative error. Several two hidden layers Artificial Neural Networks (ANN) architectures with back propagation algorithm and momentum learning process were applied using Matlab software. They used strain, strain rate and temperature as input and flow stress as output. It was found that an optimum architecture of 3-9-10-1 shows proper prediction with respect to the conventional regression method, i.e., the relative error reached -0.13 % in place of 11.52 %. This ANN method is also capable of generating high precision output for unseen deformation conditions if proper initial weights and biases are used.

关键词： flow stress prediction CrMoV steel conventional regression method artificial neural network back propagation algorithm

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Neural network modelling of Abbott-Firestone roughness parameters in honing processes

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INTERNATIONAL JOURNAL OF SURFACE SCIENCE AND ENGINEERING 2017年第6期11卷 512-530页

作者： Sivatte-Adroer, Maurici Buj-Corral, Irene Llanas-Parra, Xavier Univ Politecn Cataluna Dept Mech Engn Sch Engn Vilanova I la Geltru EPSEVG Av Victor Balaguer 1 Barcelona 08800 Spain Univ Politecn Cataluna Dept Mech Engn Sch Engn Barcelona ETSEIB Av Diagonal 647 E-08028 Barcelona Spain Univ Politecn Cataluna Sch Engn Vilanova I la Geltru EPSEVG Automat Control Dept Av Victor Balaguer 1 Barcelona 08800 Spain

In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.

关键词： surface roughness honing artificial neural networks ANN backpropagation algorithm Abbott-Firestone roughness parameters back propagation algorithm grain size density of abrasive linear speed tangential speed pressure

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