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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...

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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Prediction of Arc Voltage of Electric Arc Furnace Based on Improved Back Propagation Neural Network

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SN Computer Science 2021年第3期2卷 167页

作者： Vinayaka, K.U. Puttaswamy, P.S. Siddaganga Institute of Technology Karnataka Tumakuru India GSSSIETW Karnataka Mysore India

The Production of the quality steel is achieved by melting iron and steel scraps by utilizing electric power in an electric arc furnace, it acts as one of the major troublesome load in the electric power system due to high nonlinear and chaotic nature, thereby creating severe power quality disturbances to the interconnected network, therefor the need for model to describe the behavior of electric arc furnace in a simulation of electrical power system become significant, the relation between the arc voltage and arc current is pivotal in defining the characteristics of an electric arc furnace, in this paper, the Voltage and current characteristics based on real time data is discussed, then arc voltage prediction using improved back propagation neural network based on arc current under different zones of operation are simulated using MATLAB/SIMULINK, the results infer the validation of test results with the actual values and effectiveness of prediction by adoption of improved back propagation algorithm and efficient in terms of prediction with reduced errors. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： bp algorithm Electric arc furnace Neural network Prediction model

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Application and Development of Neural Network Technology in Mechanical Automation Processing Parameters 17

Application and Development of Neural Network Technology in ...

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17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Wang, Kai Henan Coll Ind & Informat Technol Jiaozuo 454000 Henan Peoples R China

ISBN: (纸本)9781728186160

With the continuous in-depth study of metal cutting mechanism and the development of computer technology, people have established a computer-aided optimization program system for cutting data, which provides new methods and means for selecting the optimal cutting parameters. As a new technology in the field of artificial intelligence research, neural networks have non-linear characteristics and information distribution. When dealing with multiple input and multiple output systems, it eliminates the complicated correlation analysis of various variables required by traditional modeling methods. The purpose of this article is to study the application and development of neural network technology for mechanical automation processing parameters. This article trains the sample set, learns the statistical law of the sample set, and saves the learned information in the weight. When the non-sample set mode is input, the bp network in the ideal neural network is highly nonlinear. The mapping ability is not limited by the number of inputs and outputs. In specific research applications, the original program can be freely modified as needed. This paper uses bp network as a research tool, trains bp network by using a large amount of experimental data, studies and analyzes several influencing factors of error remapping phenomenon, and uses bp network to solve the basic method of remapping problem, and initially established the feasibility of the method. Experimental research shows that this article is the ideal output data (actual data) for network testing and the network output data. From these data, it can be seen that the network training output and the ideal output error are controlled below 5%. It can be seen that the training result of the network is successful.

关键词： Neural Network Mechanical Automation Parameter Development bp algorithm

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Online intelligent fault diagnosis of redundant sensors in PWR based on artificial neural network

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Yin, Yuan Xu, Fenqin Pang, Bo Shenzhen Univ SZU Coll Phys & Optoelect Engn CPOE Dept Nucl Sci & Technol Shenzhen Peoples R China Inst Nucl Power Operat Safety Technol Natl Energy R&D Ctr Nucl Power Operat &Life Manage Shenzhen Peoples R China

Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structures to improve system safety and reliability. However, reliability of the actual system is often lower than that obtained by theoretical calculation due to the inevitable occurrence of common mode fault (CMF), which is a dependent failure event that can cause multiple failures in redundant channels. CMF may increase the reliability deviation of the system by orders of magnitude and, hence, seriously affects the reliability of the system. To mitigate the CMF of redundant sensors in nuclear power plants, an artificial neural network (ANN) can serve as a data-driven analytic model to monitor sensor parameters, to identify any possible abnormal status of the sensors, and provide an early warning. In this study, by using the high-fidelity dataset obtained in a full-scope PWR simulator as training, validation, and test data, a relevant parameter-based ANN black-box model (RPANN) was established by employing the back-propagation (bp) learning algorithm, which was then defined as an analytic redundancy. Time series-based ANN checking models (TSANNs) were also established for each of the input and output parameters of the RPANN in order to identify its abnormal state based on historical data in the past. When combined with the existing hardware redundancy, the ANN-based analytic redundancy can serve as an online monitoring tool of the hardware status and an online diagnosis strategy for sensor faults. Furthermore, ANN-based analytic redundancy can replace faulty hardware sensors to analytically reconstruct the reading of the monitored sensor parameter without having to reduce the reactor output power or even shut down the reactor for emergency maintenance so that the on-site calibration frequency of hardware sensors in redundant channels can be effectively reduced. This is not only of vital importance in reducing operation and maintenance costs of existing PWR powe

关键词： common mode failure artificial neural network fault diagnosis CPR1000 full-scope PWR simulator bp algorithm

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Behavioral-level modeling of GaN HEMT small-signal intrinsic noise based on DE-SVR algorithm

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MICROELECTRONICS JOURNAL 2022年 123卷 105426-105426页

作者： Wang, Shaowei Zhang, Jincan Yang, Shi Li, Na Wang, Jinchan Liu, Min Henan Univ Sci & Technol Elect Engn Coll Luoyang 471023 Peoples R China Novaco Microelect Technol Ltd Nantong 226000 Peoples R China

The Differential Evolution-Support Vector Regression (DE-SVR) algorithm is designed to model the small-signal intrinsic noise behavior of GaN HEMT. It not only overcomes the local minimization shortcoming of the Back Propagation (bp) algorithm, but also uses the DE (Differential Evolution) algorithm to obtain the best parameter c (punishment factor) and parameter g (variance of kernel function) of the Support Vector Regression (SVR) algorithm. In order to validate the superiority of the DE-SVR algorithm, the experiment compares the modeling effects of bp algorithm, SVR algorithm, and DE-SVR algorithm in modeling the small-signal intrinsic noise model of GaN HEMT. The experimental results show that there are obvious advantages for the DE-SVR algorithm in modeling the small-signal intrinsic noise characteristics of GaN HEMT.

关键词： DE-SVR algorithm bp algorithm SVR algorithm GaN HEMT small-Signal intrinsic noise model

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Improved bp Arithmetic in Moisture Content Measurement with Microwave Resonant 8

Improved BP Arithmetic in Moisture Content Measurement with ...

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8th IEEE International Conference on Computer Science and Network Technology (ICCSNT)

作者： Liu, Zhi Henan Univ Sci & Technol Elect Engn Coll Luoyang Peoples R China

ISBN: (纸本)9781728181233

Traditional linear regression is the primary factor that affects measurement precision in measuring moisture content with microwave resonator. A regression is put forward based on an improved bp algorithm to modify the measurement result. First, the regression neural network is pre optimized by using the macro search ability, parallel operation and strong robustness of genetic algorithm. Then, integrating the gradient descent method of bp algorithm, the presented algorithm can effectively avoid the traditional bp algorithm of falling into local minimum, at the same time, high prediction accuracy and fast convergence speed are maintained. It has the characteristics of global superiority and accuracy for optimization, thus improving the measurement accuracy. The experimental results show that the mean square error between predicted moisture and actual moisture is 0.0109, the average absolute error is 0.0702, the average relative error is 0.1161, and the determination coefficient is 0.9989.

关键词： moisture content open microwave resonant bp algorithm genetic algorithm(GA)

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Learning to Decode Polar Codes With One-Bit Quantizer

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IEEE ACCESS 2020年 8卷 27210-27217页

作者： Gao, Jian Niu, Kai Dong, Chao Beijing Univ Posts & Telecommun Key Lab Univ Wireless Commun Minist Educ Beijing 100876 Peoples R China

A deep learning method for improving the performance of polar belief propagation (bp) decoder equipped with a one-bit quantizer is proposed. The method generalizes the standard polar bp algorithm by assigning weights to the layers of the unfolded factor graph. These weights can be learned autonomously using deep learning techniques. We prove that the improved polar bp decoder has a symmetric structure, so that the weights can be trained by an all-zero codeword rather than an exponential number of codewords. In order to accelerate the training convergence, a layer-based weight assignment scheme is designed, which decreases the amount of trainable weights. Simulation results show that the improved polar bp decoder with a one-bit quantizer outperforms the standard polar bp decoder with a 2-bit quantizer and achieves faster convergence.

关键词： Polar codes one-bit decoder deep learning bp algorithm

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GPU-Based Fast Imaging Scheme for GB-MIMO Radar

GPU-Based Fast Imaging Scheme for GB-MIMO Radar

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2021 CIE International Conference on Radar, Radar 2021

作者： Hou, Sican Ren, Xiaoyu Tian, Weiming Deng, Yunkai Dong, Xichao Beijing Institute of Technology Key Laboratory of Electronic and Information Technology in Satellite Navigation Ministry of Education Beijing China Beijing China Radar Research Lab Beijing China

ISBN: (纸本)9781665498142

GB-MIMO (Ground-Based Multiple Input Multiple Output) radar plays an important role in landslide disaster prevention. Common MIMO radar imaging based on bp (Back Projection) algorithm is slow, and severely affects the real-time performance of deformation measurement. In this paper, a fast imaging scheme applied for GB-MIMO radar is proposed based on a heterogeneous system consisting of CPU (Center Processing Unit) and GPU (Graphic Processing Unit). The signal processing architecture is firstly designed according to the radar imaging principle and algorithm analysis. Then the acceleration technology of GPU is studied with CUDA technology. The results show that the computing speed achieves 30∼35 times in three different echo sizes less than conventional schemes, which proves that the method can meet the requirement of real-time well. © 2021 IEEE.

关键词： bp algorithm Fast Imaging GB-MIMO Radar GPU

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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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Quantum Neural Network with Improved Quantum Learning algorithm

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INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS 2020年第7期59卷 1978-1991页

作者： Chen, Bu-Qing Niu, Xu-Feng PLA Informat Engn Univ Teaching & Res Sect Polit Luoyang 471003 Henan Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710121 Shaanxi Peoples R China

We present a quantum bp neural network with the universality of single-qubit rotation gate and two-qubit Controlled-NOT gate. Also, we show the process of the bp learning algorithm for the quantum model, and propose an improved bp learning algorithm based on quantum genetic algorithm. The type recognition simulation of the Matlab program shows the efficiencies of the quantum neural network and the improved learning algorithm.

关键词： Quantum neural network bp algorithm Quantum genetic algorithm

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