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7th International Conference on Machine Learning and Cybernetics

作者： Ling, Ming-Qing Liu, Wei-Wei Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

ISBN: (纸本)9781424420957

Intrusion detection system is a hot spot in the researching domain of information security. The paper puts forward an intrusion detection model called lmBP. Firstly the paper does a research on different optimal algorithms of BP (Back Propagation) networks and then combines improved BP algorithm with Levenberg-Marquardt algorithm to device lmBP. Based on this model the lm-HIDS intrusion detection system is established. The result of the experiment shows that it is an efficient way to detect host intrusion by applying Levenberg-Marquardt algorithm to optimize BP network.

关键词： information security intrusion detection neural network lm algorithm

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RESEARCH ON INTRUSION DETECTION SYSTEMS BASED ON LEVENBERG-MARQUARDT algorithm

RESEARCH ON INTRUSION DETECTION SYSTEMS BASED ON LEVENBERG-M...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： MING-QING LING WEI-WEI LIU School of Computer and Information Technology Beijing Jiaotong UniversityBcijing 100044 China Air School of Computer and Information Technology Beijing Jiaotong UniversityBcijing 100044 China

关键词： Information security Intrusion detection Neural Network lm algorithm

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ANN-based estimator for distillation using Levenberg-Marquardt approach

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2007年第2期20卷 249-259页

作者： Singh, Vijander Gupta, Indra Gupta, H. O. Indian Inst Technol Dept Elect Engn Roorkee 247669 Uttaranchal India

In modern chemical industries the purity of the distillate is the main objective and time to estimate the distillate composition is also the constraint. In the present paper, the Levenberg-Marquardt (lm) approach is proposed for predictive inferential control of distillation process. The developed estimator using lm approach predicts the composition of distillate using column pressure, reboiler duty, and reflux flow along with the temperature profile of the distillation column as inputs. In complex chemical industries where the output depends on many parameters, Steepest Descent Back Propagation (SDBP) algorithm does not work properly for estimating the composition of distillate, which results in saturated outputs and differs from the desired results. To overcome such type of situation, lm approach is used in developed estimator. The estimated results are compared with the simulation results and it is observed that the results obtained from lm approach are significantly improved than the results obtained from SDBP algorithm. To enhance the accuracy of the estimated results, the pressure, reflux flow and heat input with temperature profile of the column are used as input to train the neural network. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： distillation process inferential measurements ANN (artificial neural network) approach lm algorithm artificial intelligence

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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 Reliability-Based SNR Estimation Method in Turbo Detection Systems

A Reliability-Based SNR Estimation Method in Turbo Detection...

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IEEE Vehicular Technology Conference

作者： Li Qiang Shaoqian Li Nion Key Lb of Counicion Universiy of Eecronic Science nd Technoogy of Chin Chengdu 610054 P.R. Chin

ISBN: (纸本)1424402638;9781424402632;1424402646

In this paper, we analyze the sensitivity of MAP algorithm and its reduced ones, as Max-Log-MAP and Scaled Max-Log-Map, to SNR mismatch in turbo detection systems. And then, we propose a new reliability-based SNR estimation method using the soft extrinsic information output from SMlm decoder. Next, a new turbo equalization scheme is also provided with the new SNR estimation algorithm. Finally, the performance of turbo equalization systems are evaluated using our new scheme. The simulation results show that our equalization scheme performs very close to the lm algorithm with known channel SNR, although it does not need pilot symbols.

关键词： equalisers maximum likelihood detection maximum likelihood estimation turbo codes lm algorithm MAP algorithm SMlm decoder log maximum a posteriori algorithm reliability-based SNR estimation method scaled max-log-map soft extrinsic information output turb Equalizers maximum likelihood detection turbo codes maximum likelihood estimation Detection systems Least mean square algorithm Worm gears Turbo

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Optimized arithmetic used in garbage power generation plants addressing

Optimized arithmetic used in garbage power generation plants...

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5th International Conference on Machine Learning and Cybernetics

作者： Huang, Yuan-Sheng Zheng, Yan Luo, Gang N China Elect Power Univ Sch Business Adm Baoding 071003 Peoples R China N China Elect Power Univ Sch Business Adm Beijing 102206 Peoples R China

ISBN: (纸本)1424400600

Neural network has ability of self-studying, self-adapting, fault tolerance and generalization. However, there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. So there are some limitations in practice. in order to avoid these shortages, the paper solves these problems from two aspects. One is to adopt principle component analysis to select study samples and to make some of them containing more sample characteristics;the other is to train the network by using Levenberg-Marquardt backward propagation algorithm. Finally, an example is used to prove the new method is of high effectiveness and practicality in solving the addressing problem of garbage power generation plants.

关键词： garbage power generation plant lm algorithm neural network location selection principle component analysis

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OPTIMIZED ARITHMETIC USED IN GARBAGE POWER GENERATION PLANTS ADDRESSING

OPTIMIZED ARITHMETIC USED IN GARBAGE POWER GENERATION PLANTS...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YUAN-SHENG HUANG YAN ZHENG GANG LUO School of Business Administration North China Electric Power University Baoding 071003 China School of Business Administration North China Electric Power University Beijing 102206 China

Neural network has ability of self-studying, self-adapting, fault tolerance and generalization. However, there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. So there are some limitations in practice. In order to avoid these shortages, the paper solves these problems from two aspects. One is to adopt principle component analysis to select study samples and to make some of them containing more sample characteristics;the other is to train the network by using Levenberg-Marquardt backward propagation algorithm. Finally, an example is used to prove the new method is of high effectiveness and practicality in solving the addressing problem of garbage power generation plants.

关键词： Garbage power generation plant lm algorithm Neural network Location selection Principle component analysis

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Parameter identification by neural network for intelligent deep drawing of axisymmetric workpieces

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2005年第3期166卷 387-391页

作者： Zhao, J Wang, FQ Yanshan Univ Coll Mech Engn Qinhuangdao 066004 Peoples R China Shougang Res Inst Technol Beijing 100041 Peoples R China

Intelligent deep drawing for axisymmetric workpieces is an important research field of intelligent sheet metal forming, and real-time identification of parameters is a key technology for intelligent deep drawing. This paper presents a feed-forward neural network model based on the lm algorithm (put forward by Levenberg and Marquardt), which is established to realize real-time identification of material properties and friction coefficient for deep drawing of an axisymmetric workpiece. Compared with the previous BP model (neural network based on back propagation algorithm) and GA-ENN (evolutionary neural network based on genetic algorithm) model, the error goal of parameter identification by the lm model is stepped downward to a new level. Therefore, accurate parameter identification, which provides preconditions as well as assurance for accurate prediction and control, lays the basis for intelligent deep drawing of sheet metal. (c) 2004 Elsevier B.V. All rights reserved.

关键词： intelligent deep drawing parameter identification neural network lm algorithm

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Application of Optimized BP Neural Network in Addressing for Garbage Power Plant

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Electricity 2005年第A4期16卷 52-55页

作者： By Zheng Yan, Huang Yuansheng, Qi Jianxun and Tang Jing School of Business Administration, North China Electric Power University School of Electrical Engineering, North China Electric Power University School of Business Administration North China Electric Power University School of Electrical Engineering North China Electric Power University

Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects. One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.

关键词： garbage power plant lm algorithm neural network site selecdon principle component analysis

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Artificial neural network based voltage stability evaluation

Artificial neural network based voltage stability evaluation

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IASTED International Conference on Artificial Intelligence and Applications

作者： Modi, PK Singh, SP Sharma, J Indian Inst Technol Alternate Hydro Energy Ctr Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)0889864047

Voltage stability has been a major concern for the power system utility, because of several events of voltage collapse in past decade. It becomes therefore, necessary to develop tools for fast determining the voltage stability state of the power system. With the developments of FACTS devices it has been possible for the power system to operate closer to its operational limit. In this paper, loadability margin, which is defined as the distance from operating point to voltage collapse point, is calculated using artificial neural network for the power system with Static Var Compensator (SVC). Multi layer feed forward neural network is proposed. To reduce the size of neural network, input features are selected by employing system entropy method. All ac limits are considered. The proposed method is applied to IEEE-30 bus and IEEE-118 bus system. The results obtained are promising in terms of accuracy and speed.

关键词： voltage stability FACTS devices loadability margin neural network static Var Compensator lm algorithm

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