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Metering equipment running error estimation model based on genetic optimized lm algorithm

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第1期22卷 197-205页

作者： Chen, Liang Huang, Youpeng Lu, Tao Dang, Sanlei Kong, Zhengmin Guangdong Power Grid Co Ltd Meteorol Ctr Guangzhou Guangdong Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Hubei Peoples R China

The current method of smart meter verification relies on manual regular sampling inspection, which is heavy in workload and poor in real-time, and can't fully monitor all the equipments. Therefore, a remote real-time error monitoring algorithm is indispensable. We propose a smart meter error estimation model based on genetic optimized Levenberg-Marquarelt (lm) algorithm. Firstly, based on the law of conservation of energy, the relationship between smart meter error and electricity consumption is established. Then, lm algorithm is optimized based on genetic algorithm and used to estimate the operating error of electricity meter. Finally, we used the actual data of the pilot cities in a province for the experiment. The results show that the proposed method can effectively improve the accuracy of smart meter error estimation.

关键词： Smart meter AMI data running error remote estimation genetic algorithm 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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Performance Analysis of Compression Techniques Using lm algorithm and SVD for Medical Images 6

Performance Analysis of Compression Techniques Using LM Algo...

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6th International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Rani, Laxmi Prasanna M. Rao, Sasibhushana G. Rao, Prabhakara B. MVGR Coll Engn Dept ECE Vizianagaram Andhra Pradesh India AU Coll Engn A Dept ECE Visakhapatnam Andhra Pradesh India JNT Univ Dept ECE Kakinada Andhra Pradesh India

ISBN: (纸本)9781728113807

This paper presents the techniques for compression and decompression of MRI, CT and X-ray images. These medical images are the pictorial representation of inner parts of human body which are used for the analysis of critical diseases. As the vast amount of data is required to store medical images for future reference of the patients and for the transmission, there is need to use image compression methods without reducing the quality of information. Two methods of compression i.e. back propagation neural network with lm training algorithm (BPNNlm) and Singular Value Decomposition (SVD) are used in this paper. The results of these two techniques are compared with respect to the performance metrics of Peak Simal to Noise Ratio (PSNR), Mean Squared Error and Structural Similarity Index Measurement (SSIM). From the results, it is observed that SVD image compression technique based on singular values provides more PSNR, less MSE and better SSIM values compared to BPNNlm Technique.

关键词： Singular Valuee Decomposition Bach propagation Neural Network lm algorithm Peak Signal to Noise Ratio Mean Squared Error Structural Similarity Index Measurement

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Application of Improved BP Neural Network Based on lm algorithm in Desulfurization System of Thermal Power Plant

Application of Improved BP Neural Network Based on LM Algori...

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Chinese Automation Congress (CAC)

作者： Cheng, Huanxin Cui, Lijie Li, Jing Qing Dao Univ Sci & Technol Sch Automat & Elect Engn Qing Dao Peoples R China

ISBN: (纸本)9781538635247

Use flue gas wet desulphurization technology to reduce the emissions of sulfur dioxide. The improved BP neural network model based on lm algorithm was established according to the factors that influence sulfur dioxide emissions, and the network weights and threshold value were adjusted repeatedly. The real data obtained from thermal power plants are simulated and verified. The results show that the improved BP network prediction model of lm algorithm has higher prediction accuracy of desulfurization efficiency, and has certain guiding significance for field operation.

关键词： flue gas wet desulphurization improved BP neural network lm algorithm desulfurization efficiency

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Inversion of Neural Network Rayleigh Wave Dispersion Based on lm algorithm

Inversion of Neural Network Rayleigh Wave Dispersion Based o...

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Jiao Jian Lin Jun Zhou Xiao-hua Lu Hao Jilin Univ Coll Instrumentat & Elect Engn Changchun 130026 Peoples R China

For the standard neural network algorithm has defects such as: local minima and slow convergence rate, etc., an iterative inversion method of neural network Rayleigh wave based the stable and fast levenberge-marquardt (lm) algorithm is put forward. This method increases the network training rate and reduces the times of iterative inversion through numerical optimization and verifies the effectiveness of the method through iterative inversion of the medium model. According to the comparison and analysis, it indicates that the method reduces the times of iterative inversion and increases the convergence rate obviously. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： lm algorithm neural network Rayleigh wave iterative inversion

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Application of Improved BP Neural Networks based on lm algorithm in Characteristic Curve Fitting of Fiber-optic Micro-bend Sensor

Application of Improved BP Neural Networks based on LM Algor...

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4th International Conference on Advances in Materials and Manufacturing (ICAMMP 2013)

作者： Ding Shuo Chang Xiao-heng Bohai Univ Coll Engn Jinzhou 121013 Liaoning Provin Peoples R China

ISBN: (纸本)9783038350156

As a key factor in a testing system, sensor nonlinearity has always been the study focus in the field of engineering and techniques. In order to accurately reflect the practical characteristics of a fiber-optic micro-bend sensor, Levenberg-Marguardt (lm) algorithm is used to optimize the correction of the weight values of standard back propagation neural network (BPNN). The learning process of improved BPNN based on lm algorithm (lm-BPNN) is also illustrated mathematically, and lm-BPNN is applied in fitting the input and output characteristic curve of a fiber-optic micro-bend sensor. The simulation results show that lm-BPNN is superior both in its convergence rate and fitting precision over standard BPNN.

关键词： Fiber-optic micro-bend sensor lm algorithm Back propagation neural network Curve fitting Simulation

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Metabolic Variations in Grass C. dactylon and Selection of Optimal LEDs for the Horticulture Luminaire Using lm algorithm

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IEEE ACCESS 2021年 9卷 139457-139465页

作者： Sowmya, R. Narasimhan, S. Kurian, Ciji Pearl Srividya, R. Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 India Manipal Acad Higher Educ Manipal Inst Technol Dept Biotechnol Engn Manipal 576104 India

Healing of various ailments using herbal medicines is gaining much interest. Plants classified as grasses, specifically Cynodon dactylon, are an appreciated group of monocots used in many herbal remedies. In this work, C. dactylon, is grown naturally and also under market available LED Luminaires with different lighting conditions. Until 2010, most of the plants are grown under conventional lamps that are not spectrally tunable. Cynodon dactylon, the grass is grown under two different light spectrum, two light levels and three photoperiods (9hours, 12 hours, 15 hours) to extend our experiential knowledge. The biomass accumulation was the highest when grown under a lower RB ratio-12-hour- $163\mu $ mol/s, and phenolic content was the highest at 92.8 mg/g wt Gallic Acid Equivalents under combined light source at 15-hour photoperiod. A spectrally tunable LED light source with an optimal quantity of LEDs saves cost, space and energy. Considering the light parameters from the light sources used for growing C. dactylon, Levenberg-Marquardt (lm) algorithm is implemented to select an optimal LED quantity that composes the light source. The algorithm simulates the given target spectrum with minimum fitness error. The method applied to model LEDs, its validation against the practical LED spectrum, spectrum matching and computation of Luminous flux, Photosynthetic Photon Flux and efficacy are also presented. Many spectrums are simulated to validate the performance of the algorithm. A solution of optimal LEDs for three Photosynthetic Photon Flux (PPF) levels with LEDs is derived, and it is observed that the number of LEDs increased with PPF.

关键词： LED lamps photosynthetic active radiation photosynthetic photon efficiency spectral power density applied optimization lm algorithm

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Improved phase-generated carrier demodulation technique based on lm algorithm with low harmonic distortion and high stability

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OPTICAL FIBER TECHNOLOGY 2024年 87卷

作者： Zhou, Mingyuan Yu, Benli Shi, Jinhui Zhou, Wen Fang, Chongxu Gong, Yuanfei Zuo, Cheng Zhang, Jihao Anhui Univ Key Lab Optoelect Informat Acquisit & Manipulat Minist Educ Jiulong Rd 111 Hefei 230601 Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China

In this paper, a multi-harmonic mixing demodulation scheme based on Levenberg-Marquardt (lm) algorithm, called PGC-lm-Arctan, is proposed to eliminate the influence of phase modulation depth on demodulation results. The relation between the C value and ratio of Bessel function is fitted by lm algorithm, and then the ratio of fourth harmonic and second harmonic is substituted into the above relation. The C value is calculated to eliminate the influence of coefficients on the demodulation results in Arctan algorithm. The experimental results show that when the modulation depth is from 1.5 rad to 4.8 rad, the harmonic distortion of the improved algorithm is lower than 0.7 %, which keeps the best level of the traditional PGC algorithm. The signal-to-noise-anddistortion-ratio is 41.69 dB, which is 17.58 dB higher than the traditional PGC-Arctan algorithm.

关键词： Fiber-optic interferometric sensor Phase generated carrier (PGC) demodulation Phase modulation depth Arctangent technology lm algorithm

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Inversion of Neural Network Rayleigh Wave Dispersion Based on lm algorithm

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Procedia Engineering 2011年 15卷 5126-5132页

作者： JIAO Jian LIN Jun ZHOU Xiao-hua LU Hao College of Instrumentation and Electrical Engineering Jilin UniversityChangchun 130026China

关键词： lm algorithm neural network Rayleigh wave iterative inversion

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Parameter Identification of Creep Model of Rock Based on Modified PSO-lm algorithm

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ELECTRONIC JOURNAL OF GEOTECHNICAL ENGINEERING 2016年第16期21卷 5389-5395页

作者： Song Yong-jun Ren Jian-xi Zhang Kun Liu Chao-ke Chen Xing-zhou Xian Univ Sci & Technol Coll Architecture & Civil Engn Xian 710054 Shaanxi Peoples R China

Identification of the creep parameter is one of the important research issues in the field of rock creep mechanics. Considered the defect of parameter identification in creep model which the particle swarm optimization (PSO) is convergence slowly, and prone to fall into local optimum solution, the Levenberg-Marquardt nonlinear least squares method (lm) is relied heavily on the initial value. Therefore, a new parameter identified method is proposed that it combined with linear decreasing weight particle swarm optimization(modified PSO) and lm method, which proceeds as: 1) the model parameters are identified by means of modified PSO firstly;2) lm method is used to identify the model parameters by initial values from step 1. A case showed that the modified PSO-lm method could be effectively identified the parameters of rock creep model.

关键词： Rock mechanics Creep model Identification of parameter Particle swarm optimization lm algorithm

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