检索结果-内蒙古大学图书馆

bp neural network prediction-based variable-period sampling approach for networked control systems

APPLIED MATHEMATICS AND COMPUTATION 2007年第2期185卷 976-988页

作者： Yi, Jianqiang Wang, Qian Zhao, Dongbin Wen, John T. Chinese Acad Sci Inst Automat Lab Complex Syst & Intelligence Sci Beijing 100080 Peoples R China Techfaith Wireless Commun Technol Beijing Co Ltd Beijing 100016 Peoples R China Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

The biggest problem that networked control systems face is the random time-varying delay, which often causes system instability and even collapse. Aiming at this problem, a new modeling scheme for the networked control systems, motivated from a variable-period sampling approach, is presented in this paper. Here, the time delay to occur at current sampling step is taken as the sampling period between current sampling step and next sampling step. To predict online the time delay induced in the networked control systems, a bp feedforward neural network is adopted and the training algorithm of the bp neural network is given. To make the bp neural network adapt to the changing environment of the networked control systems and improve its prediction accuracy, the bp neural network is designed to further update according to its prediction error after each prediction. At each sampling step, good approximation to actual time delay becomes available and different sampling period is obtained. Control simulations using the variable sampling period and fixed sampling period are compared. Simulation results show that this new approach can alleviate the influence of time delay to the greatest extent and improve the performance of the networked control systems. (c) 2006 Elsevier Inc. All rights reserved.

关键词： bp algorithm control networked control system neural network modeling prediction sampling

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Application of genetic bp network to discriminating earthquakes and explosions

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Acta Seismologica Sinica(English Edition) 2002年第5期15卷 540-549页

作者： BIAN Yin-ju(边银菊) Institute of Geophysics China Seismological Bureau Beijing 100081 China

We developed a GA-bp algorithm by combining the genetic algorithm (GA) with the back propagation (bp) algorithm and established a genetic bp neural network. We also applied the bp neural network based on the bp algorithm and the genetic bp neural network based on the GA-bp algorithm to discriminate earthquakes and explosions. The obtained result shows that the discriminating performance of the genetic bp network is slightly better than that of the bp network.

关键词： artificial neural network bp algorithm genetic algorithm

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Control of Switched Reluctance Motors based on Improved bp Neural Networks

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2018年第2期11卷 97-102页

作者： Xiao Jin-Feng Xiao Qi-Ming Univ South China Sch Elect Engn Hengyang 421001 Hunan Peoples R China Foxconn Wireless Business Grp Hengyang 421001 Hunan Peoples R China

Background: Switched reluctance motors have a strong nonlinear performance due to their structure and operation mode. The performance and control strategy of this kind of motor are obviously different from those traditional strategies. As a result, the accurate model and high performance control of the switched reluctance motor prove to be very important and has obtained wide researches. Method: A kind of switched reluctance motor based on PID neural network control strategy is proposed, which combines artificial fish swarm and particle swarm optimization to optimize weights and thresholds of bp neural networks. Results: Speed responses of the improved bp algorithm have no overshoot, have a smooth transition to the steady state and eliminate the oscillation phenomena which is in the PID control. Conclusion: Besides, it reduces time of transient process to improve the response speed. Anti interference ability and robustness are obviously superior to the PID control.

关键词： Switched reluctance motor bp neural network artificial fish swarm particle swarm optimization bp algorithm global optical value

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Analysis of the electrical properties of PZT by a bp artificial neural network

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COMPUTATIONAL MATERIALS SCIENCE 2005年第2期34卷 166-172页

作者： Cai, K Xia, JT Li, LT 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

Artificial neural networks (ANN) constitute a recently emerged intriguing data processing technique. Over the last decade chemistry became a field of their wide application. Nevertheless, few has been known of the application of ANN modeling technique in inorganic ceramic materials. Based on the homogeneous experimental design the experimental results of 21 samples were analyzed by a three-layer bp (back propagation) network. Influence of the additives on the properties of the materials were illustrated by the registered ANN model. The basic reasons for the relationships between the additives and the electrical properties of the materials produced by the ANN model were also explained by the doping theory for piezoelectric ceramics. The optimized formulation was calculated and examined. The precise prediction results indicate that the three-layer bp network based modeling is a practically very useful tool in both property analysis and formulation design of the multicomponent oxide ceramic material. (c) 2005 Elsevier B.V. All rights reserved.

关键词： ceramics PZT piezoelectric properties artificial neural network bp algorithm

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STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON bp NEURAL NETWORK

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Chinese Journal of Mechanical Engineering 2003年第4期16卷 441-444页

作者： Zhang Cuiping Yang QingfoCollege of Mechanical Engineering,Taiyuan University of Technology,Taiyuan 030024, China College of Mechanical Engineering Taiyuan University of Technology Taiyuan 030024 China

According to advantages of neural network and characteristics of operatingprocedures of engine, a new strategy is represented on the control of fuel injection and ignitiontiming of gasoline engine based on improved bp network algorithm. The optimum ignition advance angleand fuel injection pulse band of engine under different speed and load are tested for the samplestraining network, focusing on the study of the design method and procedure of bp neural network inengine injection and ignition control. The results show that artificial neural network technique canmeet the requirement of engine injection and ignition control. The method is feasible for improvingpower performance, economy and emission performances of gasoline engine.

关键词： Neural network bp algorithm Gasoline engine Control

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Establishment of constitutive relationship model for 2519 aluminum alloy based on bp artificial neural network

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Journal of Central South University of Technology 2005年第4期12卷 380-384页

作者：林启权彭大暑朱远志 School of Mechanical Engineering Xiangtan University Xiangtan 411105 ChinaSchool of Materials Science and Engineering Central South University Changsha 410083 China School of Materials Science and Engineering Central South University Changsha 410083 China School

An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(bp) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.

关键词： 2519 aluminum alloy bp algorithm neural network constitutive model

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Main steam temperature control based on GA-bp optimised fuzzy neural network

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INTERNATIONAL JOURNAL OF ENGINEERING SYSTEMS MODELLING AND SIMULATION 2017年第3期9卷 150-160页

作者： Tian, Zhongda Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

The high inertia and long time-delay characteristics of main steam temperature control system in a thermal power plant will reduce the system control performance. In order to solve this problem, a genetic algorithm-back propagation (GA-bp) optimised fuzzy neural network control strategy is proposed in this paper. Gauss function is chosen as membership function and fuzzy neural network is designed. GA combined with bp algorithm is chosen for the offline parameters optimisation of fuzzy neural network, and then bp algorithm is used for online parameters optimisation. GA-bp optimisation algorithm overcomes the shortcomings of GA algorithm or bp algorithm which is used to adjust the parameters of fuzzy neural network controller. The simulation experiment compared with cascade PID and fuzzy neural network is carried out. Simulation results show that the controller based on GA-bp optimised fuzzy neural network has faster response speed, smaller overshoot and error, better tracking performance, and reduces the lag effect of the control system under different load, working conditions and membership functions.

关键词： main steam temperature genetic algorithm bp algorithm fuzzy control fuzzy neural network

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Simulation Study of Vehicle Speed Variation Based on bp Neural Network PID Control 20

Simulation Study of Vehicle Speed Variation Based on BP Neur...

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20th COTA International Conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Xu, Yu Zheng, Xuan Fu, Shuai Wang, Zhiheng Liu, Haoxue Changan Univ Sch Automobile Xian 710064 Shaanxi Peoples R China Changan Univ Sch Elect & Control Engn POB 710064 Xian Peoples R China Zhenjiang Transportat Comprehens Adm Law Enforcem POB 212000 Zhenjiang Jiangsu Peoples R China

ISBN: (数字)9780784482933

ISBN: (纸本)9780784482933

Because the driver's speed control system is a system with strong time-varying and large variation of parameters, in order to simulate the change rule of the driver's speed, this paper establishes the bp neural network PID model. In this paper, a three-layer bp neural network is designed and compared with the simulation curve of the speed variation of the classical PID control and the speed variation simulation curve of the PID control of the bp neural network in the PID control system. The simulation results of this paper show that the bp neural network PID controller designed in this paper shows good control effect in the performance dynamic following, real-time and robustness.

关键词： Artificial neural network bp algorithm PID control Speed control system

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A Forecast Model Based on The bp Neural Network Used in Refinery's Steel Equipment's Corrosion

A Forecast Model Based on The BP Neural Network Used in Refi...

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4th International Conference on Computer Science and Education

作者： Ma, Zhuo Lin, Kunhui Jiang, Xiangmin Zhou, Changle Liu, Han Xiamen Univ Software Sch Xiamen 361005 Fujian Peoples R China Xiamen Univ Dept Comp Sci Xiamen 361005 Fujian Peoples R China

ISBN: (纸本)9781424435197

The forecasting of the corrosion of refinery's steel equipments shows great importance in preventing the accident. Considering the numerous factors affecting the corroding of refinery's steel equipments, which are uneasily predictable and with complex relationships, this paper proposed a new technology based on the bp neural network technology used in forecasting of the corrosion of refinery's steel equipments. A new model is also built and implemented in this paper. Finally, the experimental results prove the feasibility of the new model and the forecasted results by this new model fixes well with the sample data set.

关键词： neural network bp algorithm corrosion

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Sedimentary Micro-phase Automatic Recognition Based on bp Neural Network

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Journal of Donghua University(English Edition) 2004年第3期21卷 98-102页

作者：龚声蓉王朝晖 School of Computer Science and Technology Soochow University Suzhou 215006

In the process of geologic prospecting and development, it is important to forecast the distribution of gritstone, master the regulation of physical parameter in the reserves mass level. Especially, it is more important to recognize to rock phase and sedimentary circumstance. In the land level, the study of sedimentary phase and micro-phase is important to prospect and develop. In this paper, an automatic approach based on ANN (Artificial Neural Networks) is proposed to recognize sedimentary phase, the corresponding system is designed after the character of well general curves is considered. Different from the approach extracting feature parameters, the proposed approach can directly process the input curves. The proposed method consists of two steps: The first step is called learning. In this step, the system creates automatically sedimentary micro-phase features by learning from the standard sedimentary micro-phase patterns such as standard electric current phase curves of the well and standard resistance rate curves of the well. The second step is called recognition. In this step, based the results of the learning step, the system classifies automatically by comparing the standard pattern curves of the well to unknown pattern curves of the well. The experiment has demonstrated that the proposed approach is more effective than those approaches used previously.

关键词： neural networks bp algorithm sedimentary micro-phase

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