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International Joint Conference on Neural Networks

作者： manh C. Phan mark H. Beale martin T. Hagan School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 USA

ISBN: (纸本)9781467361279

In this paper, we introduce a new procedure for efficient training of recurrent neural networks. The new procedure uses a batch training method based on a modified version of the levenberg-marquardt algorithm. The information of gradients of individual sequences is used to mitigate the effect of spurious valleys in the error surface of recurrent networks. The method is tested on the modeling and control of several physical systems.

关键词： Training Recurrent Networks individual sequences modeling recurrent network modeling error curve Batch Training Functional training l-m algorithm

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Combining Genetic algorithm and levenberg-marquardt algorithm in Training Neural Network for Hypoglycemia Detection using EEG Signals

Combining Genetic Algorithm and Levenberg-Marquardt Algorith...

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Annual International Conference of the IEEE Engineering in medicine and Biology Society

作者： lien B. Nguyen Anh V. Nguyen Sai Ho ling Hung T. Nguyen Centre for Health Technologies Faculty of Engineering and Information Technology University of

ISBN: (纸本)9781457702150

Hypoglycemia is the most common but highly feared complication induced by the intensive insulin therapy in patients with type 1 diabetes mellitus (T1Dm). Nocturnal hypoglycemia is dangerous because sleep obscures early symptoms and potentially leads to severe episodes which can cause seizure, coma, or even death. It is shown that the hypoglycemia onset induces early changes in electroencephalography (EEG) signals which can be detected non-invasively. In our research, EEG signals from five T1Dm patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected efficiently using EEG signals from only two channels. This paper demonstrates that by implementing a training process of combining genetic algorithm and levenberg-marquardt algorithm, the classification results are improved markedly up to 75% sensitivity and 60% specificity on a separate testing set.

关键词： l-m algorithm Hypoglycemia Genetic algorithm (GA) Feature extraction insulin replacement therapy Neural network TEST SETS Fast Fourier transform Type 1 Diabetes mellitus

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Accelerating Nonlinear Diffusion Tensor Estimation For medical Image Processing Using High Performance GPU Clusters

Accelerating Nonlinear Diffusion Tensor Estimation For Medic...

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IEEE International Conference on Application-specific Systems, Architectures and Processors

作者： Vinh Q. Dang Esam El-Araby lam H. Dao lin-Ching Chang Department of Electrical Engineering and Computer Science The Catholic University of America Washington DC 20064 USA

ISBN: (纸本)9781479904945

Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance technique that produces in vivo images of biological tissues with local microstructural characteristics such as water diffusion. It can be used, for example, to localize white matter lesions, or in neuronavigation surgery of brain tumors. Diffusion tensor maps are usually computed on a voxel-by-voxel basis by fitting the signal intensities of diffusion weighted images as a function of their corresponding data acquisition parameters. This processing is highly computation-intensive and can be time-consuming which constraints the clinical use of DTI. This study presents the application of using high performance GPU clusters in diffusion tensor estimation by accelerating the multivariate non-linear regression. The results are tested in simulated DTI brain datasets and show significant performance gain in tensor fitting in addition to favorable scalability characteristics. The proposed GPU implementation framework can further promote the clinical use of DTI, and can be used to accelerate statistical analysis of DTI where monte Carlo simulations are employed, or readily applied to quantitative assessment of DTI using bootstrap analysis.

关键词： diffusion tensor imaging (DTI) nonlinear diffusion tensor estimation levenberg-marquardt algorithm GPU high performance clusters Diffusion Tensor Imaging l-m algorithm GRAPPER PICK UP Graphics Processing Unit medical image processing Tensor acceleration Signal Intensity HIGH-PERFORmANCE diffusion

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Application of the l-m optimized algorithm to predicting blast vibration parameters

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Earthquake Science 2008年第5期21卷 542-547+554页

作者：张艺峰姚道平谢志招杨江峰叶友权 Xiamen Seismic Survey Research Center Institute of Geophysical Prospecting SINOPEC Research Institute of Petroleum Exploration and Development

Currently,the regression empirical formula with vibration peak as the safety index of single blasting vibration is widely applied in blasting engineering circles throughout the *** to complex blasting mechanism

关键词： blasting vibration neural network l-m algorithm prediction

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Application of Improved lm-BP Neuron Network in stock prediction

Application of Improved LM-BP Neuron Network in stock predic...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： lei Zhang Bofeng Zhang Yajun Gu Computer Department Shanghai TV University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9781467329637

The prediction of stock closing quotation is very difficult because the time series seem random but not random completely. So a Back-Propagation Neurons Network based on Improved levenberg-marquardt algorithm was presented in this paper. The nonlinear mapping characteristic of the Back-Propagation Neural Network makes it can approach to every function in any precision. The Improved levenberg-marquardt algorithm can accelerate the speed of convergence, so it is easy to approach to the global optimal solution, not the local optimal solutions and. The prediction experiment shows that the Improved levenberg-marquardt algorithm works better than traditional Auto-regressive moving-Average model.

关键词： Back-Propagation Neurons Network levenberg-marquardt algorithm Stock Prediction l-m algorithm Neurons Time series back propagation Random

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Research on The Prediction of Ice Accretion on UHV Transmission lines - Based on BP Neural Network

Research on The Prediction of Ice Accretion on UHV Transmiss...

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2012 International Conference on Electrical and Computer Engineering(ICECE2012)

作者： Kai Wang 1,a,Yi luo 1,b,Yi Yao 1,c,Jing Huang 2,d 1 Dept.of Elecronic Engineering Sichuan University of Since & Engineering Zigong,Sichuan 643000,P.R.China 2 Dept.of Elecronic Engineering Guangxi Normal University Guilin,Guangxi 541004,P.R.China

In order to improve the prediction accuracy of ice accretion on UHV transmission lines,a new method based on BP neural network which has nonlinear relation highly was proposed in this *** BP network ameliorated by the l-m algorithm was used to predict the ice accretion on transmission lines,the prediction error less 1mm accounts for 60 percents of *** simulation result shows that the ANN model is able to forecast the ice accretion on power transmission lines *** this paper we succeed in predicting the complex ice accretion process with a highly nonlinear model,the BP neural network.

关键词： UHV transmission line ice accretion BP neural network l-m algorithm prediction

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Study on the Application of lmBP Neural Network in Hall micro-displacement Sensor Cueve Fitting

Study on the Application of LMBP Neural Network in Hall Micr...

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2012 International Conference on Computer Science and mathematics,Physical Education and management（ICCmPE 2012）

作者： Shuo Ding College of Engineering Bohai University Jinzhou, China Qinggong Hu College of Engineering Bohai University Jinzhou, China

As a key factor in a testing system, sensor nonlinearity has always been the study focus in the field of engineering and techniques. The output curve of a hall micro- displacement sensor shows apparent nonlinearity due to the effect of temperature, moisture and other environmental factors. In order to accurately reflect the practical characteristics of hall micro-displacement sensor, levenberg- marguardt algorithm is employed to optimize the correction of BP weight value. A fast convergence lmBP learning algorithm is proposed. The learning process of lmBP algorithm is also illustrated mathematically, and a network is designed via mATlAB to fit the hall micro-displacement sensor characteristic curve. The simulation result shows that lmBP network is superior both in its convergence rate and approximation capability over traditional BP network and improved BP network.

关键词： lmBP neural network l-m algorithm hall micro-displacement sensor curve fitting mATlAB language

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On The Application of BP Neural Network Based On levenberg-marquardt algorithm In The Diagnosis of mental Disorders

On The Application of BP Neural Network Based On Levenberg-M...

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International Conference on Consumer Electronics, Communications and Networks

作者： Yujie Cui Hailing Xiong Kai Zheng Jianbang Chen College of Computer and Information Science Southwest University

ISBN: (纸本)9781457714146

Because of the traditional BP neural network's limitation, including easily falling into local minimum, slow convergence and iteration for a long time, we propose a diagnostic system of mental disorders based on levenberg-marquardt algorithm. This article illustrates the basic principles of the neural network algorithm and establishes a mental diagnosis model based on l-m algorithm which uses common mental illness among college students as subjects. The outcome of the simulation illustrates the fact that it is effective to apply the neural network to establish a mental diagnostic system because it has reduced the training iterations and training period and it has high accuracy as well.

关键词： BP neural network l-m algorithm mental disorder

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A Convergent algorithm for Generalized linear Complementarity Problem In Engineering modeling

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International Conference on materials Engineering for Advanced Technologies (ICmEAT2011)

作者： Sun, Hongchun Linyi Univ Sch Sci Linyi 276005 Shandong Peoples R China

ISBN: (纸本)9783037851517

In this paper, we establish a error bound for the generalized linear complementtarity problem in engineering modeling(GlCP)which can be viewed as extensions of previously known results, based on which the famous levenberg-marquardt (l-m) algorithm is employed for obtaining its solution, and we show that the l-m algorithm is quadratically convergent without nondegenerate solution which is a new result for GlCP.

关键词： Generalized linear complementtarity problem engineering modeling error bound l-m algorithm quadratical convergence nondegenerate solution

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A Convergent algorithm for Generalized linear Complementarity Problem In Engineering modeling

A Convergent Algorithm for Generalized Linear Complementarit...

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The 2011 International Conference on materials Engineering for Advanced Technologies(ICmEAT 2011)

作者： Hongchun Sun School of Sciences Linyi University

In this paper, we establish a error bound for the generalized linear complementtarity problem in engineering modeling(GlCP)which can be viewed as extensions of previously known results, based on which the famous levenberg-marquardt(l-m) algorithm is employed for obtaining its solution, and we show that the l-m algorithm is quadratically convergent without nondegenerate solution which is a new result for GlCP.

关键词： Generalized linear complementtarity problem engineering modeling error bound l-m algorithm quadratical convergence nondegenerate solution.

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