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Precision motion control of permanent magnet linear motors

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2007年第3-4期35卷 301-308页

作者： Zhang, Dailin Chen, Youping Ai, Wu Zhou, Zude Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

In the paper, a method of precision motion control for permanent magnet linear motors is proposed. Unlike rotational motors, permanent magnetic linear motors are more sensitive to various force disturbances because of the reduction of gears. So, as a feedback compensator, a disturbance observer is used to compensate the force disturbances based on the disturbance model. But the force disturbances of permanent magnetic linear motors cannot be fully compensated owing to the error of dynamic model, inaccurately detected velocity and acceleration, especially when a permanent magnetic linear motor runs in low speed. Further analysis shows that the force ripple is the main force disturbance when the velocity of a PMLM is close to zero, and the disturbance model denotes that the force ripple is position dependent. In order to further suppress the force disturbances of permanent magnetic linear motors a feedforward neural network using the bp algorithm is proposed to approximate and compensate the force ripple. The experimental results show that the force ripple is efficiently alleviated and the high positioning precision can be achieved by using the proposed precision motion control method.

关键词： bp algorithm neural network disturbance observer PMLM motion control

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Fuzzy neural networks-based quality prediction system for sintering process

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2000年第3期8卷 314-324页

作者： Er, MJ Liao, J Lin, JY Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Strateg Technol Pte Ltd Singapore 609917 Singapore Zhejiang Univ State Key Lab Fluid Power Transmiss & Control Hangzhou 310027 Peoples R China

In this paper, based on the property of sintering process, a hybrid fuzzy neural networks (FNN) and genetic algorithm (GA) system is proposed to solve the difficult and challenging problem of constructing a system model from the given input and output data to predict the quality of chemical components of the finished sinter mineral. A bidirectional fuzzy neural network (BFNN) is proposed to represent the fuzzy model and realize the fuzzy inference. The learning process of BFNN is divided into off-line and on-line learning. In off-line learning, the GA is used to train the BFNN and construct a system model based on the training data. During on-line operation, the algorithm inherited from the principle of backpropagation is used to adjust the network parameters and improve the system precision in each sampling period. The process of constructing a system model is introduced in details. The results obtained from the actual prediction demonstrate that the performance and capability of the proposed system are superior.

关键词： bp algorithm fuzzy neural network genetic algorithm iron making sintering production

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A three-dimensional advancing front technique to generate grids based on the neural networks

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ARCHIVE OF APPLIED MECHANICS 2024年第11期94卷 3389-3404页

作者： Liu, Hanlin Wang, Nianhua Cui, Huimin Zhang, Zhen Han, Zhiming Liu, Qingkuan Shijiazhuang Tiedao Univ Dept Math & Phys Shijiazhuang 050043 Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang 621000 Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang 050043 Peoples R China Innovat Ctr Wind Engn & Wind Energy Technol Hebei Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Sch Civil Engn Shijiazhuang 050043 Peoples R China

In computational fluid dynamics, controlling grid scale is efficiently managed using the Advancing Front Technique (AFT). However, achieving grid generation convergence within a three-dimensional (3D) computational domain remains challenging, primarily due to excessive intersection judgments that significantly reduce efficiency. This paper addresses the non-convergence issues inherent in the 3D AFT and proposes preliminary solutions to enhance algorithm robustness while reducing intersection judgments. We introduce two neural networks trained on the backpropagation (bp) algorithms, Line-ANN and Plane-ANN, specifically designed for integration with AFT. These networks are individually combined with traditional 3D AFT to develop two enhanced methods. We assess these methods by comparing grid quality and time consumption against traditional AFT approaches. The results demonstrate that integrating Plane-ANN and Line-ANN with AFT improves overall efficiency by approximately 55% and 36%, respectively, thereby significantly enhancing grid generation efficiency.

关键词： Computational fluid dynamics Machine learning bp algorithm AFT Grid generation

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Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation algorithm for Information Services

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Computers, Materials & Continua 2020年第5期63卷 605-619页

作者： Junhua Xi Kouquan Zheng Jianfeng Ma Jungang Yang Zhiyao Liang College of Information and Communication National University of Defense TechnologyXi’an710106China Macao University of Science and Technology TaipaMacao

Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production *** order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems,a new Petri net modeling method is proposed by introducing bp(Error Back Propagation)algorithm in neural *** judging whether the transition is ignited by continuous function,the intuitionistic fuzziness of classical bp algorithm is extended to the parameter learning and training,which makes Petri network have stronger generalization ability and adaptive function,and the reasoning result is more accurate and credible,which is useful for information ***,a typical example is given to verify the effectiveness and superiority of the parameter optimization method.

关键词： Intuitionistic fuzzy set intuitionistic fuzzy Petri nets production rule bp algorithm

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Kikuchi approximation method for joint decoding of LDPC codes and partial-response channels

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IEEE TRANSACTIONS ON COMMUNICATIONS 2006年第7期54卷 1149-1153页

作者： Pakzad, Payam Anantharam, Venkat Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Ecole Polytech Fed Lausanne Sch Comp & Commun Engn CH-1015 Lausanne Switzerland

In this letter, we apply the Kikuchi approximation method to the problem of joint decoding of a low-density parity-check code and a partial-response channel. The Kikuchi method is, in general, more powerful than the conventional loopy belief propagation (bp) algorithm, and can produce better approximations to an underlying inference problem. We will first review the Kikuchi approximation method and the generalized bp algorithm, which is an iterative message-passing algorithm based on this method. We will then report simulation results which show that the Kikuchi method outperforms the best conventional iterative method.

关键词： .Gbp message-passing marginals belief propagation Partial-Response LDPC Codes bp algorithm Joint Decoding

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Modeling and analysis of the electrical properties of PZT through neural networks

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JOURNAL OF THE EUROPEAN CERAMIC SOCIETY 2003年第12期23卷 2177-2181页

作者： Guo, D Li, LT Nan, CW Xia, JT Gui, ZL Tsing Hua Univ Dept Mat Sci & Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Chem Engn & Mat Beijing 100081 Peoples R China

Application of ANN (Artificial neural network) to the electrical properties analysis of PZT is discussed in this paper. The same set of results of PZT samples were analyzed by a back-propagation (bp) network in comparison with a multiple nonlinear regression analysis (MNLR) model. The results revealed that the ANN model is much more accurate than MNLR model. The ANN approach also gave quite encouraging predictions for formulations not included in the train set samples, indicating that the bp network is a very useful and accurate tool for the properties analysis and prediction of multi-component solid solution piezoelectric ceramics. (C) 2003 Elsevier Science Ltd. All rights reserved.

关键词： neural networks bp algorithm piezoelectric properties PZT modeling

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Application of Artificial Neural Network in Robotic Hybrid Position/Force Control

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High Technology Letters 1996年第1期2卷 26-29页

作者：陈卫东 Robot Research Institute Harbin Institute of TechnologyHarbin 150001P.R.China

A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 *** hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of *** experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.

关键词： Robotic hybrid position/force control Adaptive PID control Feedforward network bp algorithm Training pattern

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Small-signal behavioral-level modeling of InP HBT based on SO-bp neural network

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SOLID-STATE ELECTRONICS 2023年 209卷

作者： Dong, Jianping Su, Yongbo Mei, Bo Yang, Feng Han, Xinlong Jin, Zhi Zhong, Yinghui Zhengzhou Univ Sch Phys & Microelect Zhengzhou 450001 Peoples R China Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China China Acad Space Technol CAST Beijing 100029 Peoples R China

In this paper, Snake Optimizer-Back Propagation (SO-bp) neural network algorithm has been proposed to model the small-signal characteristic of InP heterojunction bipolar transistors (HBTs). The SO algorithm can effectively improve the problem of local optima encountered by the bp neural network algorithm. The InP HBT small-signal models have been constructed by using Back Propagation (bp) algorithm, the Genetic algorithm-Back Propagation (GA-bp) algorithm and the Snake Optimizer-Back Propagation (SO-bp) algorithm. The experimental results verify that the proposed SO-bp neural network algorithm model has the lowest error rate between simulated and measured data. Hence, the SO-bp algorithm is a more effective approach for modeling the small-signal characteristic of InP HBT. The accurate small-signal behavioral-level model of HBT has significant reference value for the application of microwave and millimeter-wave integrated circuits (MMICs).

关键词： InP HBT Small-signal model bp algorithm GA-bp algorithm SO-bp algorithm

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Recurrent Neural Network Model for Prediction of Microclimate in Solar Greenhouse

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IFAC-PapersOnLine 2018年第17期51卷 790-795页

作者： Hongkang, Wang Li, Li Yong, Wu Fanjia, Meng Haihua, Wang Sigrimis, N.A. Key Laboratory on Modern Precision Agriculture System Integration Research of Ministry of Education China Agriculture University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture China Agriculture University Beijing100083 China Department of Natural Resources Management & Agricultural Engineering Agricultural University of Athens Athens11855 Greece

A recurrent neural network(RNN) based dynamic back propagation(bp) algorithm model with historical internal inputs are developed to predict the temperature and humidity of a solar greenhouse in the north of China. Climate data including air and substrate temperature, air humidity, illumination and C02 concentration recorded over eight days were used to build and validate models for climatic prediction. In order to compare the accuracy of predictions, different performance measures, such as average relative error (ARE), mean absolute error (AME) and root mean square error (RSME), were calculated, for using bp and untrained RNN neural networks using the same processing. For the RNN model, a context layer as the last hidden layer output, is input with the next input to the next hidden layer, which is equivalent to the state feedback. The results demonstrate that the RNN-bp model provides reasonably good predictions with the RSME for temperature 0.751 and 0.781 for humidity, including both of the R2 is both above 0.9, which outperforms the compared models tested in this paper. © 2018

关键词： Recurrent neural networks Backpropagation Climate models Errors Forecasting Greenhouses Mean square error Predictive analytics Solar heating State feedback bp algorithm Dynamic back propagation Microclimate Prediction model Recurrent neural network (RNN) Recurrent neural network model Solar greenhouse Temperature and humidities

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Nonlinear stretching for enhanced learning of the backpropagation algorithm in target recognition

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 1997年第6期16卷 334-337页

作者： Ge, Y Cha, SYS Zhang, JJ Zhang, L He, AZ Department of Mechanical Engineering University of Illinois at Chicago Chicago Illinois 60607 Department of Applied Physics Nanjing University of Science and Technology Nanjing P.R. China

In spite of its relatively slow learning speed, backpropagation (bp) is one of the most popular neutral network training algorithms. Here, a method based on nonlinear stretching is presented that modifies the activation function in a bp algorithm to speed up the convergence in training. A target recognition system that incorporates the approach and moment invariants is formulated and tested. The test results indicate that the speed of convergence can be effectively increased through appropriate selection of a stretch factor. (C) 1997 John Wiley & Sons, Inc.

关键词： target recognition bp algorithm nonlinear stretching moment invariants

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