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Asia-Pacific Conference on Information Processing (APCIP 2009)

作者： Wang, Zhilong Gong, Zengtai Zhao, Weigang Zhu, Wenjin Lanzhou Univ Sch Math & Stat Lanzhou 730000 Peoples R China Lanzhou Polytech Coll Dept Basic Courses Lanzhou 730050 Peoples R China Northwest Normal Univ Sch Math & Inforamt Sci Lanzhou 7330070 Peoples R China

ISBN: (纸本)9780769536996

This paper presents a higher-order multivariate Markov chain model combined with particle swarm optimization algorithm. Due to some deficiencies, such as only considering the maximum probability while ignoring the effect of the other probabilities, the traditional method of probability distribution has been replaced by the level characteristics value of fuzzy set theory;further more particle swarm optimization algorithm has been employed to optimize the coefficient of level characteristics value. In recent years, air pollution acutely aggravates chronic diseases in mankind, such as sulfur dioxide pollution which plays a most important role in acid rain. In order to confront air pollution problems and to plan abatement strategies, both the scientific community and the relevant authorities have focused on monitoring and analyzing the atmospheric pollutants concentration. Taking the forecast of air pollutants as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows the new method is predominant in forecasting of multivariate and non-linear data.

关键词： Mean-Standard deviation division method Higher-order multivariate Markov chain Level characteristics value particle swarm optimization algorithm

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Support Vector Regression and particle swarm optimization algorithm for Intelligent Electronic Circuit Fault Diagnosis

Support Vector Regression and Particle Swarm Optimization Al...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Tian, WenJie Tian, Yue Ai, Lan Liu, JiCheng Beijing Union Univ Automat Inst Beijing Peoples R China Univ Cape Economics & Business Finance Inst Beijing Peoples R China

ISBN: (纸本)9781424445189

In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. Additionally, the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit fault. Then, we compared the proposed PSOA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.

关键词： fault diagnosis rough set particle swarm optimization algorithm support vector regression electronic circuit

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ARIMA Model Estimated by particle swarm optimization algorithm for Consumer Price Index Forecasting

ARIMA Model Estimated by Particle Swarm Optimization Algorit...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Wang, Hongjie Zhao, Weigang Lanzhou Jiaotong Univ Railway Tech Coll Lanzhou 730000 Peoples R China

ISBN: (纸本)9783642052521

This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of AMNIA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.

关键词： ARIMA model particle swarm optimization algorithm Moment estimation Consumer price index

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Application of Optimized Neural Network Based on particle swarm optimization algorithm in Fault Diagnosis

Application of Optimized Neural Network Based on Particle Sw...

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8th IEEE International Conference on Cognitive Informatics (ICCI 2009)

作者： Zhong, Bingxiang Wang, Debiao Li, Taifu Chongqing Univ Sci & Technol Coll Elect Informat Engn Chongqing 401331 Peoples R China

ISBN: (纸本)9781424446421

In this paperan algorithm based on particle swarm optimization algorithm for RBF neural network is propose. With particle swarm optimization algorithm, neural network weights are optimized. Also through the dynamic regulation of the number of radial basis function in neural network hidden layer, neural network structure is optimized. The algorithm is applied to gearbox fault diagnosis. Experimental results show the effectiveness and great performance. Classification effect of neural network based on particle swarm optimization algorithm is better than that of the RBF neural network for identifying effectively the different status of gearbox and monitoring timely the status changes of gearbox. Also it can reduce the time for fault diagnosis and improve accuracy of fault diagnosis.

关键词： particle swarm optimization algorithm RBF neural networks Fault diagnosis Gearbox

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An objective prediction model for typhoon rainstorm using particle swarm optimization: neural network ensemble

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NATURAL HAZARDS 2014年第2期73卷 427-437页

作者： Zhao, Hua-sheng Jin, Long Huang, Ying Jin, Jian Guangxi Climate Ctr Nanning 530022 Peoples R China E China Normal Univ Dept Comp Sci & Technol Shanghai 200062 Peoples R China

A nonlinear ensemble prediction model for typhoon rainstorm has been developed based on particle swarm optimization-neural network (PSO-NN). In this model, PSO algorithm is employed for optimizing the network structure and initial weight of the NN with creating multiple ensemble members. The model input of the ensemble member is the high correlated grid point factors selected from the rainfall forecast field of Japan Meteorological Agency numerical prediction products using the stepwise regression method, and the model output is the future 24 h rainfall forecast of the 89 stations. Results show that the objective prediction model is more accurate than the numerical prediction model which is directly interpolated into the stations, so it can better been implemented for the interpretation and application of numerical prediction products, indicating a potentially better operational weather prediction.

关键词： Typhoon rainstorm Objective prediction particle swarm optimization algorithm Neural network Interpretation and application

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A New Fuzzy Identification Approach Using Support Vector Regression and particle swarm optimization algorithm

A New Fuzzy Identification Approach Using Support Vector Reg...

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： Tian, WenJie Tian, Yue Beijing Union Univ Automat Inst Beijing Peoples R China Capital Univ Econ & Business Finance Inst Beijing Peoples R China

ISBN: (纸本)9781424442461

A new fuzzy identification approach using support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved PSOA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalization ability.

关键词： particle swarm optimization algorithm fuzzy system identification positive definite reference function support vector regression

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Research on the Dynamic Configuration of Air-Based Pseudolite Network 6th

Research on the Dynamic Configuration of Air-Based Pseudolit...

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China Satellite Navigation Conference (CSNC) 2015 Proceedings, VolII

作者： Li, Da-peng Deng, Ping-ke Liu, Bing Qu, Yi Zeng, Ling-chuan Liu, Ting Chinese Acad Sci Acad Optoelect Beijing Peoples R China

ISBN: (纸本)9783662466353;9783662466346

To realize the adaptive adjustment of the air-based pseudo-lite (PL) navigation augmentation network, a dynamic configuration method for the air-based PL network deployment is proposed. By surveying and defining the indicators describing the performance of navigation augmentation network, based on the basic needs of navigation enhancement task, an objective function is designed for the optimization deployment of the air-based PLs. Using particle swarm optimization algorithm (PSO) for the fine search of the objective function of the air-based PL optimization deployment in multi-dimension, the dynamic optimal deployment is obtained. The simulation results show that, the proposed method could keep the navigation performance optimum, in the case of navigation enhancement service region changing or the local PL being interfered.

关键词： Air-based pseudo-lite Navigation enhancement Dynamic configuration of network particle swarm optimization algorithm

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Three-dimensional Mobile Node Localization Method of WSNs Based on Improved LSSVR algorithm 7

Three-dimensional Mobile Node Localization Method of WSNs Ba...

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Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2015)

作者： Zhang Lieping Ping, Wang Fei, Peng Peng, Cao Guilin Univ Technol Coll Mech & Control Engn Guilin 541004 Guangxi Peoples R China Guilin Univ Technol Bowen Coll Management Guilin 541006 Guangxi Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guilin 541006 Guangxi Peoples R China

ISBN: (纸本)9781467371438

The traditional least squares support vector regression (LSSVR) node localization algorithm for wireless sensor networks(WSNs) uses the average hop distance to calculate the actual distance, which may result larger localization error in the obstacle conditions. An improved LSSVR WSNs three-dimensional mobile node localization method in an obstacle conditions was proposed in this paper. The average per hop distance of four anchor nodes closest was used to replace the average distance per hop of traditional LSSVR algorithm in the proposed method, and the new average per hop distance was used to calculate the measurement distance of each unknown node to anchor nodes. The LSSVR localization model was built through sampling of the grid and constructing the training sets. According to mean square deviation of predicted location of virtual nodes and their actual location, fitness function was constructed, and LSSVR kernel function and regularization parameters were optimized by the PSO algorithm. The simulation results show that, compared with the conventional LSSVR localization algorithm, the proposed localization algorithm has a higher localization accuracy, smaller localization errors and lower localization cost in the obstacle conditions.

关键词： Wireless Sensor Networks Localization of Three-dimensional Mobile Nodes Least Squares Support Vector Regression particle swarm optimization algorithm Obstacle Conditions

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Dynamic Compensation for Infrared Thermometer Based on Wiener Model and particle swarm optimization algorithm

Dynamic Compensation for Infrared Thermometer Based on Wiene...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Tian, WenJie Tian, Yue Ai, Lan Liu, JiCheng Beijing Union Univ Automat Inst Beijing Peoples R China Univ Cape Economics & Business Finsen Inst Beijing Peoples R China

ISBN: (纸本)9781424445189

A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer, because of which the dynamic performance of the thermometer is effectively improved. The dynamic compensator is established and the compensation is described and explicated by the Wiener model. According to Wiener model, the novel structure is devised. The identification of thermometer non-linear dynamic compensator is achieved by particle swarm optimization algorithm. The results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.

关键词： Wiener model compensation particle swarm optimization algorithm infrared thermometer identification

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The Flow Capturing Location Model and algorithm of Electric Vehicle Charging Stations

The Flow Capturing Location Model and Algorithm of Electric ...

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International Conference on Logistics, Informatics and Service Sciences (LISS)

作者： Lin, Wanting Hua, Guowei Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China

ISBN: (纸本)9781479918911

As a necessary supporting infrastructure in development of electric vehicles, electric vehicle charging stations can provide electric vehicles charging service. Their locations are reasonable or not directly related to the development of the electric vehicle industry and their service quality, efficiency, convenience, etc. On the basis of flow capturing location model, this paper treats intercepting the largest demand as the goal and uses particle swarm optimization algorithm to simulate the model to prove its effectiveness and practicability. The simulation result shows that the model and algorithm used in this paper can establish an optimal selection of location, and be able to provide some decisions to the real facility location.

关键词： electric vehicles facility location flow capturing location model particle swarm optimization algorithm

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