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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Pang, Zaixiang Wang, Tongyu Liu, Shuai Wang, Zhanli Gong, Linan Changchun Univ Sci & Technol Sch Mechatron Engn Changchun 130022 Peoples R China Changchun Univ Technol Sch Mechatron Engn Changchun 130022 Peoples R China Changchun Vocat Inst Technol Changchun 130000 Peoples R China

ISBN: (纸本)9781728159225

To solve the inverse kinematics problem of 7-DOF upper limb rehabilitation training robot, propose a new solution method based on BP neural network. Taking a 7-DOF upper limb rehabilitation training robot as the research object, carry out the forward kinematics analysis, establish the BP neural network model for solving the inverse kinematics and improve the neural network. Finally, MATLAB is used to simulate and verify, the simulation results show that the improved BP neural network model can solve the inverse kinematics of 7-DOF upper limb rehabilitation training robot, avoid the complex problem of traditional inverse solution calculation, and the solution process is simple;compared with the standard BP neural network, the learning convergence speed is faster and the solution precision is higher, so it is a feasible 7-DOF inverse kinematics solution method for upper limb rehabilitation training robot.

关键词： 7-DOF upper limb rehabilitation robot Inverse kinematics solution neural network lm algorithm

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Prediction of Stock Index Based on BP Neural Network

Prediction of Stock Index Based on BP Neural Network

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作者： Zhun Jiang Peng Zhu School of Economics and Management Nanjing University of Science and Technology

With the continuous development of China's economy in recent years, more and more investors are devoted in stock market and the changes of stock market are related to the vital interests of every shareholder. In the mature market economy, the index related to stock price and trading volume is the important basis for investors to make decisions and the barometer of the national economy at the same time. Therefore, the method to effectively forecast the stock index is particularly important. First, the paper introduces the basic situation of Shanghai Composite Index and Shenzhen Composite Index. Then we make the relevant introduction of BP neural network. Next,we introduce how to construct the BP(Back Propagation) neural network model optimized by lm(Levenberg-Marquard) algorithm according to the nonlinear characteristics of the stock market for further forecasting the closing prices of Shanghai Composite Index and Shenzhen Composite Index in order to test the validity of BP neural network in stock forecasting by the comparison between forecasts and actual stock prices, to provide some help and reference for investors' investment and government's macro-control.

关键词： Stock index forecast BP neural network lm algorithm

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Research on Prediction Model of Bending Force Based on BP Neural Network with lm algorithm

Research on Prediction Model of Bending Force Based on BP Ne...

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第25届中国控制与决策会议

作者： Xiaohua Li shashi Liu Hui Li Jing Wang Tao Zhang School of Electrons and Information Engineering University of Science and Technology Liaoning School of Control Science and Engineering Dalian University of Technology

ISBN: (纸本)9781467355339

Hydraulic bending roller is a most basic and important method for shape control of strip. The rolled shape quality is decided by the setting value of bending farce in great part. This paper chooses five-stand hot tandem rolling mill in 1810 product line of Tangshan Iron and Steel Company as background, and deals primarily with the study of the bending force prediction model of the rolling unit. To counter the imperfection of traditional prediction model and according to feature of hot strip mill, the various factors influencing bending farce are analyzed, and a bending farce prediction model based on BP neural network with lm algorithm is set up. The training and testing simulation for the neural network is done by using the actual production data of hot rolled steel SS400. By means of the analysis toward simulation results, it is shown that the neural network prediction model for bending force has not only a fast convergence speed, but also a high prediction accuracy to meet actual production request. The research provides a direction and foundation for the setting of practical bending force of 1810 hot rolling line.

关键词： Bending force prediction model BP neural network lm algorithm

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X-ray radiographs reconstruction based on nonlinear least squares with deconvolution

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2019年 929卷 134-141页

作者： Wang, Zhongmiao Jing, Yuefeng Kang, Xu Ding, Tong Liu, Jun China Acad Engn Phys Inst Fluid Phys POB 919-105 Mianyang 621900 Peoples R China

High energy X-ray radiographs reconstruction is a typical nonlinear process. However, most of the current reconstruction methods are based on a linear approximation model, which uses optical depth images for reconstruction. As radiographs are convolved by the system blur, images reconstructions with these algorithms are blurred, especially at edges. In this paper, a nonlinear least squares reconstruction model for transmittance images is established and optimized by the Levenberg-Marquardt algorithm assuming that the system blur is known. The numerical experiments show that the reconstruction error of this approach is greatly reduced and the edges sharpness is significantly improved, compared with the traditional constrained conjugate gradient method and the trust region reflective method. For images contaminated by blur and low noise, the reconstruction is still improved and acceptable even when an inaccurate system blur is used. Furthermore, we propose an idea of rectifying the system blur measured by experiments. Our reconstruction method is practical in deconvolution and shows a promising prospect in high energy X-ray radiographs reconstruction.

关键词： Radiographs reconstruction Nonlinear least squares Point spread function Deconvolution lm algorithm

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A Control algorithm for the Optimization of Batch Reactor-Based Processes

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Fluid Dynamics & Materials Processing 2019年第4期15卷 307-319页

作者： Yanling Bai Feng Liu Artificial Intelligence Institute Zhengzhou Railway Vocational&Technical CollegeZhengzhouChina Cyber Security Guard Henan Provincial Public Security DepartmentZhengzhouChina

Levenberg-Marquardt(lm)algorithm is applied for the optimization of the heat transfer of a batch *** validity of the approach is verified through comparison with experimental *** is found that the mathematical model can properly describe the heat transfer relationships characterizing the considered system,with the error being kept within±2℃.Indeed,the difference between the actual measured values and the model calculated value curve is within±1.5℃,which is in agreement with the model assumptions and demonstrates the reliability and effectiveness of the algorithm applied to the batch reactor heat transfer ***,the present work provides a theoretical reference for the conversion of practical problems in the field of chemical production into mathematical models.

关键词： Batch reactor lm algorithm parameter estimation model

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Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

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Journal on Internet of Things 2020年第3期2卷 109-120页

作者： Yanming Wang Kebin Jia Pengyu Liu Beijing University of Technology Beijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124China

With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is *** at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure *** experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.

关键词： Depth neural network pressure sensor nonlinearity correction wavelet transform lm algorithm

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Temperature Control Strategy of Incubator based on RBF Neural Network PID

World Scientific Research Journal

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World Scientific Research Journal 2020年第1期6卷 7-15页

作者： Jian Zhang Hongchang Sun Yuming Qi Sanpeng Deng

Taking the incubator temperature control system as the research object, the author studied the hardware structure of the incubator temperature control system and established the mathematical model of the temperature control system. Because the culture object is very sensitive to temperature changes, the traditional PID temperature control algorithm has slow response speed, weak anti-interference ability, and the control parameters are not easy to be set, the author adopts the strategy combining radial basis function (RBF) neural network and traditional PID to adjust the temperature of the incubator. In order to further improve the stability and dynamic response characteristics of the system, the lm algorithm was used instead of the gradient descent method to adjust the PID control parameters. The simulation results show that the control algorithm has faster response speed and stronger anti-interference ability, and can adjust the PID parameters in real time, which can meet the temperature control requirements of the incubator.

关键词： Temperature control system RBF neural network PID control lm algorithm

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LD Beam Collimation Based on Aspheric Cylindrical Lens 9

LD Beam Collimation Based on Aspheric Cylindrical Lens

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9th International Symposium on Advanced Optical Manufacturing and Testing Technologies - Advanced Optical Manufacturing Technologies

作者： Guo, Changwei Xiong, Baoxing Zhang, Guiju Xu, Qinfeng Xu, Jiawei Zhang, Yufei Zou, Kuaisheng Soochow Univ Coll Phys Optoelect & Energy Suzhou Jiangsu Peoples R China Soochow Univ Minist China Key Lab Adv Opt Mfg Technol Jiangsu Prov Suzhou 215006 Jiangsu Peoples R China Soochow Univ Minist China Key Lab Modern Opt Technol Educ Suzhou 215006 Jiangsu Peoples R China

ISBN: (纸本)9781510623194

A collimating system is designed by two orthogonal aspheric cylindrical lenses, which is used to collimate the the output beam of the Laser Diode (LD). The theory of light transmission is used to analyze the optical collimating system. The theoretical expressions are derived and the numerical results are obtained. The nonlinear Levenberg-Marquardt (lm) fitting algorithm based on the trust-region rule is applied to the surface shape evaluation. Residual analysis is used to test the rationality of the model hypothesis, and a simple analysis is made on the source of the error and the data optimization process. And the optical simulation of model optimization results is established. Simulation results show that the semidiverging angles of 20 degrees and 9 degrees for the fast and slow axes of semiconductor laser beam are both reduced to 0.05 mrad after passing the collimation system which is much better than that of a normal cylindrical lens collimating system 3 mrad. A uniform circular spot can be formed in the far field by adjusting the distance between the two aspheric lenses.

关键词： aspheric cylindrical lens semiconductor laser lm algorithm collimate geometric optics

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Prediction Model of Biogas Production for Anaerobic Digestion Process of Food Waste Based on lm-BP Neural Network and Particle Swarm algorithm Optimization

Prediction Model of Biogas Production for Anaerobic Digestio...

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Chinese Automation Congress (CAC)

作者： Li, Xiaohui Wang, Yuhong China Univ Petr Coll Informat & Control Engn Qingdao Peoples R China

ISBN: (纸本)9781538635247

This paper presents a prediction model to predict biogas production for anaerobic digestion process of food waste. There exist sophisticated biogas production models in published literature, but the application of these models in anaerobic digestion process of food waste is often impractical because of its high complexity. Considering the shortcomings of the accuracy of these biogas production prediction models, the BP neural network based on lm algorithm is researched to build biogas production prediction model. The lm algorithm is a combination of gradient descent method and Gauss-Newton method which is a more efficient optimization algorithm. And the Particle Swarm Optimization algorithm (PSO) is introduced to optimize the parameters of the model to get better prediction effect. The results show that the prediction model based on PSO-lm-BP has higher prediction accuracy compared with standard BP neural network.

关键词： Anaerobic digestion Neural network lm algorithm PSO algorithm Biogas production prediction Mathematical modelling

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Artificial Neural Network Modelling of Green Synthesised Silver Nanoparticles in Bentonite/Starch Bio-nanocomposite

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CURRENT NANOSCIENCE 2018年第3期14卷 239-251页

作者： Thapliyal, Anupama Khar, Roop Krishen Chandra, Amrish Amity Univ Amity Inst Pharm Noida India BS Anangpuria Inst Pharm Faridabad Haryana India

Background: In this study, computational Artificial Neural Network (ANN) model is applied for optimisation and evaluation of silver nanoparticles (AgNPs) size in the bio-nanocomposite matrix. The primary purpose of this study is used a feed-forward ANN model to create a connection between the output as the size of Ag-NPs, with four inputs variables, including AgNO(3 )concentration, the weight percentage of starch, Bentonite amount and Gallic acid concentration.& para;& para;Method: Silver nanoparticles were synthesised via biogenic green reduction method. The fast Levenberg-Marquardt (lm) backpropagation algorithm applied for the training of ANN model in this research. The optimised ANN is a multilayer perceptron (MLP) which is a kind of feed forward (4-10-1) network has an input layer with 4 nodes, hidden layers with 10 neurones, and an output layer with 1 node found a fitness function.& para;& para;Results: The output results of developed computational ANN model were compared to its predictive values of the size of silver nanoparticles regarding two statistical parameters, the coefficient of determination (R-2) and mean square error (MSE) of data set. It observed that ANN predicted values are close to the actual values and well fitted to the data. The mean square error(MSE) is 0.03, and a regression is about 1.& para;& para;Conclusion: AgNO3 concentration has the most likely factor affecting the size of silver nanoparticles (Ag-NPs) and this makes possible to develop a green reduction method for the preparation of silver nanoparticles. This study confirms that employing ANN method with lm feed forward (4-10-1) network is a useful tool with cost-effective for predicting the results of analysis and modelling of the chemical reactions.

关键词： Artificial neural network silver nanoparticles green-reduction method bio-nanocomposite modelling lm algorithm

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