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Transistor modeling based on lm-BPNN and CG-BPNN for the GaAs pHEMT

INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第4期37卷

作者： Lin, Qian Yang, Shuyue Yang, Ruilan Wu, Haifeng Qinghai Minzu Univ Sch Phys & Elect Informat Engn Xining Peoples R China Chengdu Ganide Technol Chengdu 610000 Peoples R China

In order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (CG) algorithm and the Levenberg-Marquardt (lm) algorithm, the CG-BPNN and lm-BPNN models of small signal for gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) are obtained and analyzed here. At first, the scattering parameters (S-parameters) of GaAs pHEMT are divided into training set and test set randomly. Experimental results show that the CG-BPNN model is better than another S-parameters when predicting ImS(12) with mean square error (MSE) of 7.6632e-06, while lm-BPNN model predicts ImS(12) with MSE of 2.4672e-06. Meanwhile, the MSE of CG-BPNN model is higher than lm-BPNN model when predicting all the S-parameters. In addition, it shows a smaller fluctuation range for the error curve of lm-BPNN model, which is more stable than the CG-BPNN model. Therefore, the double hidden layer lm-BPNN model is the better choice to characterize the small signal of GaAs pHEMT.

关键词： BPNN CG algorithm GaAs pHEMT lm algorithm modeling

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An ANN-based soft-sensor to estimate the sand content of drilling fluid 6

An ANN-based soft-sensor to estimate the sand content of dri...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Zhang, Di Duan, Longchen Xu, Yuan Gao, Hui Liu, Naipeng China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China China Univ Geosci Fac Engn Wuhan 430074 Peoples R China Qinghai Bur Environm Geol Explorat Xining 810000 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

Drilling fluid is an essential fluid in drilling work, various performance parameters of the drilling fluid directly affect the drilling efficiency and the life of the drilling equipment. In order to ensure the efficient and safe drilling process, in view of the difficulty of real-time monitoring of sand content in drilling fluids, this paper proposes an online data-driven sensor based on artificial neural network (ANN) to estimate the sand content of drilling fluid. Two artificial neural network prediction models are established, one is based on the drilling fluid viscosity, and the other is based on the Herschel-Balkeley model. The models are analyzed from the aspects of practicability and accuracy. The simulation results show that the neural network models trained by the lm algorithm can effectively predict the sand content of the drilling fluid.

关键词： Drilling fluid performance ANN neural network Online sand content estimation Herschel-Balkeley model lm algorithm

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A virtual level temperature compensation system based on information fusion technology

A virtual level temperature compensation system based on inf...

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IEEE International Conference on Robotics and Biomimetics (ROBIO 2007)

作者： Song, Zheying Liu, Chaoying Song, Xueling Zhao, Yingbao Wang, Jun Hebei Univ Sci & Technol Coll Elect Engn & Informat Sci Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781424417612

The output of ultrasonic object-location meter changes with the environmental temperature. In order to eliminate such a situation, a new information fusion algorithm based on neural network is presented. A virtual ultrasonic object-location measurement temperature compensation system is realized based on a method of the hybrid programming of MATLAB and LabVIEW with MATLAB Script node. A improved BP algorithm --lm algorithm is used to train the neural network, it can improve the data's convergent speed. Experimental results show that the method can eliminates impacts of temperature to object-location and improves the accuracy of sensor. This virtual object-location measurement system based on BP neural network is of wide and factual application value.

关键词： ultrasonic information fusion neural network LabVIEW lm algorithm

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A Genetic-algorithm-based Two-Stage Learning Scheme for Neural Networks 10

A Genetic-Algorithm-based Two-Stage Learning Scheme for Neur...

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International Conference on e-Education, e-Business, e-Management and e-Learning

作者： Wang, Shuo Zhang, Xiaomeng Zheng, Xuanyan Yuan, Bingzhi Beijing Univ Posts & Telecommun Sch Software Engn Beijing Peoples R China

ISBN: (纸本)9780769539485

In this paper, we propose A two-stage learning scheme for neural networks by integrating Gas into Structure identification In the first stage, which is also called structure identification stage, the selection of network structure and initial parameters is carried out by float genetic algorithm instead of human In the second stagewhich is called parameter identification stage the conventional optimization method is adopted to make refinements of parameters. Through the entire process, compromise is satisfactorily made among the network complexity, approximation accuracy and generalization ability

关键词： machine learning neural networks genetic algorithm lm algorithm

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Ultrasonic Parameter Estimation Using the Maximum Likelihood Estimation 4

Ultrasonic Parameter Estimation Using the Maximum Likelihood...

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2015 4th International Conference on Electrical Engineering (ICEE)

作者： Laddada, S. Lemlikchi, S. Djelouah, H. Si-Chaib, M. O. Ctr Dev Technol Avancees Baba Hassen 16303 Algeria USTHB Phys Mat Lab Bab Ezzouar Algeria UMB Lab Mecan Solides & Syst Boumerdes Algeria

ISBN: (纸本)9781467366731

The estimation of the backscattered echo was performed using the Maximum Likelihood Estimation (MLE). The backscattered ultrasonic echo is modeled as a nonlinear function of a set of parameters using the Gaussian echo model corrupted with an additive noise. Because of the white Gaussian noise (WGN) assumption, the estimation problem reduces to a least squares estimation. Thus, the nonlinear least square curve fitting with the Levenberg-Marquardt (lm) optimization algorithm was tested in the estimation procedure. The results are compared with existing results from the literature. To improve the goodness of the fit, the multivariate denoising procedure were applied as a preprocessing operation. Then, the efficiency and robustness of the lm algorithm are evaluated for both denoised and noisy signals.

关键词： Ultrasonic echo parameter estimation MLE lm algorithm Multivariate Wavelet Denoising

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Prediction of Oilfield Cementing Quality Based on Levenberg-Marquardt BP algorithm

Prediction of Oilfield Cementing Quality Based on Levenberg-...

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1st International Conference on High Performance Structures and Materials Engineering

作者： Wang Yuxue Zhou Shaohua NE Petr Univ Sch Math Sci & Engn Daqing Heilongjiang Peoples R China

In this paper an improved algorithm of BP neural network - Levenberg-Marquardt (lm) algorithm is introduced, and the simulation predictions of oilfield cementing quality is done by using this method. Finally, a practi... 详细信息

ISBN: (纸本)9783037850756

关键词： BP algorithm lm algorithm Cementing Quality Prediction

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Application of BP neural network in PSD nonlinear correction 3

Application of BP neural network in PSD nonlinear correction

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3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Yang Zhuojing Zhang Jianwei Hao Wenjie Yang Jinping MLR Ctr Hydrogeol & Environm Geol Survey China Geol S Key Lab Monitoring Technol Geol Environm Baoding Hebei Peoples R China

ISBN: (纸本)9783038352679

Because resistance of two-dimensional position sensitive detector's (PSD) photo surface is not absolute uniformity that its output is nonlinear. It is this feature enables the PSD difficult to measure small displacement. In order to solve this problem, BP neural network is proposed to solve the problem of PSD nonlinear correction after the study of traditional nonlinear correction method;BP neural network would have a strong ability of nonlinear mapping after training, and it can approach arbitrarily contact function by arbitrary precision, and MATLAB neural networking boxes can simulate BP neural network easily. Simulation and verification indicate that the method has a remarkable effect in solving nonlinear problems, and it can meet system requirements.

关键词： PSD BP neural Network Nonlinear MATLAB lm algorithm

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Learning architectures with enhanced capabilities and easier training 19

Learning architectures with enhanced capabilities and easier...

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19th IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Wilamowski, Bogdan M. Korniak, Janusz Auburn Univ Auburn AL 36849 USA Univ Informat Technol & Management Rzeszow Poland

ISBN: (纸本)9781467379397

Although discovery of the Error Back Propagation (EBP) learning algorithm was a real breakthrough, this is not only a very slow algorithm, but it also is not capable of training networks with super compact architecture. The most noticeable progress was done with an adaptation of the lm algorithm to neural network training. The lm algorithm is capable of training networks with 100 to 1000 fewer iterations, but the size of the problems are significantly limited. Also, the lm algorithm was adopted primarily for traditional MLP architectures. More recently two new revolutionary concepts were developed: Support Vector Machine and Extreme Learning Machines. They are very fast, but they train only shallow networks with one hidden layer. It was shown that these shallow networks have very limited capabilities. It has already demonstrated much higher capabilities of super compact architectures having 10 to 100 times more processing power than commonly used learning architectures For example, such a shallow MLP architecture with 10 neurons can solve only a Parity-9 problem, but a special deep FCC (Fully Connected Cascade) architecture with the same 10 neurons can solve as large a problem as a Parity-1023. Unfortunately, with the vanishing gradient problem], deep architectures are very difficult to train. By introducing additional connections across layers it was possible to efficiently train deep networks using the powerful NBN algorithm. Our early results show that there is a solution for this difficult problem.

关键词： backpropagation neural nets support vector machines EBP learning algorithm FCC architecture lm algorithm MLP architecture NBN algorithm error back propagation learning algorithm extreme learning machine fully connected cascade architecture learning architecture neural network training support vector machine Artificial neural networks Biological neural networks Computer architecture FCC Neurons Support vector machines Training Support Vector Network Least mean square algorithm Biological neural networks Neural network back propagation ARCHITECTURE Computer Architecture Federal Communications Commission (FCC) Functional training Learning machines Artificial neural networks Neurons Training

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Analysis and Synthesis of Coplanar Waveguide-Feed using Multilayer Perceptron Feed Forward Neural Networks 3

Analysis and Synthesis of Coplanar Waveguide-Feed using Mult...

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3rd International Conference on Signal Processing Communication and Networking

作者： Gunavathi, N. Sriramkumar, D. Natl Inst Technol Dept Elect & Commun Engn Tiruchirappalli 15 Tamil Nadu India

ISBN: (纸本)9781467368230

Analysis and synthesis of coplanar waveguide feed used in planar antennas have been proposed. Neural network-I has been proposed to analyze the coplanar waveguide feed. It dispenses the use of very long and complex formulae. Neural model-II has been proposed to remove the problems in conventional approach. It synthesizes, even if the dielectric constant is less than 6, unlike the previous approach. It gives the accurate result because thickness of the conductor also is taken into account unlike the conventional approach. The proposed neural networks are trained with three training algorithms like Levenberg-Marquart (lm), Conjugate Gradient of Fletcher-Powell (CGF) and Quasi-Neuron (QN). The lm algorithm is found to be the best algorithms among all based on Mean Squared Error (MSE) values of them.

关键词： analysis CPW feed lm algorithm neural network synthesis

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BP neural network and multiple linear regression in acute hospitalization costs in the comparative study

BP neural network and multiple linear regression in acute ho...

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International Conference on Intelligent Structure and Vibration Control (ISVC 2011)

作者： Wu, Jianhui Wang, Guoli Wang, Jing Su, Yu N China Coal Med Coll Div Epidemiol & Hlth Stat Hebei Prov Key Lab Occupat Hlth & Safety Coal Ind Tang Shan 063000 Peoples R China

ISBN: (纸本)9783037850206

The BP neural network is the important component of artificial neural networks, and gradually becomes a branch of the computation statistics. With its many characteristics such as large-scale parallel information processing, excellent self-adaptation and self-learning, the BP neural network has been used in solving the complex nonlinear dynamic system prediction. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. By means of contrasting the BP neural network and the multi-dimensional linear regression,this article discoveries that the BP neural network fitting ability is more stronger, the prediction performance is more stable, may be further applied and promoted in analysis and forecast of the continual material factor.

关键词： BP neural network multiple linear regression lm algorithm Prediction

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