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Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

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KNOWLEDGE-BASED SYSTEMS 2014年第0期56卷 226-239页

作者： Ren, Chao An, Ning Wang, Jianzhou Li, Lian Hu, Bin Shang, Duo Lanzhou Univ Sch Math & Stat Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Sch Informat Sci & Engn Adv Comp Lab Lanzhou 730000 Gansu Peoples R China Hefei Univ Technol Gerontechnol Lab Hefei Anhui Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China SUNY Stony Brook Coll Engn & Appl Sci Stony Brook NY 11794 USA

As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on particle Swam optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model. (C) 2013 Elsevier B.V. All rights reserved.

关键词： BP neural network Input parameters selection particle swarm optimization algorithm Wind speed Wind forecasting

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Intrusion Detection Quantitative Analysis with Support Vector Regression and particle swarm optimization algorithm

Intrusion Detection Quantitative Analysis with Support Vecto...

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International Conference on Wireless Networks and Information Systems

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Automat Inst Beijing Peoples R China

ISBN: (纸本)9780769539010

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented and used for pattern analysis of intrusion detection in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. We use rough set to reduce dimension. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.

关键词： support vector regression intrusion detection particle swarm optimization algorithm rough set convergence

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A particle swarm optimization algorithm Based on Molecule Diffusion

A Particle Swarm Optimization Algorithm Based on Molecule Di...

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International Conference on Industrial Mechatronics and Automation

作者： Liu, Xiaoxiang Jiang, Weigang Xie, Jianwen Jinan Univ Zhuhai Coll Dept Comp Sci Zhuhai Peoples R China

ISBN: (纸本)9781424438181

Inspired by the diffusion movement phenomenon of the molecule, a molecule-diffusion particle swarm optimization (MDPSO) is presented. The proposed algorithm (MDPSO) has attraction and diffusion phases. Once the diversity of population become low, the individuals will be dispersed and turn into diffusion phases, while if the diversity of population get high, the individuals carry out the attraction phases. It is indicated that MDPSO not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than SPSO by applying MDPSO to portfolio problem and comparing with SPSO and other algorithms.

关键词： molecule diffusion particle swarm optimization algorithm portfolio selection

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Hybrid particle swarm optimization algorithm for Solving Systems of Nonlinear Equations

Hybrid Particle Swarm Optimization Algorithm for Solving Sys...

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IEEE International Conference on Granular Computing

作者： Ouyang, Aijia Zhou, Yongquan Luo, Qifang Guangxi Univ Nationalities Coll Math & Comp Sci Nanning 53006 Peoples R China

ISBN: (纸本)9781424448319

A hybrid particle swarm optimization (HPSO) algorithm, which combines the advantages of Nelder-Mead simplex method (SM) and particle swarm optimization (PSO) algorithm, is put forward to solve systems of nonlinear equations, and it can be used to overcome the difficulty in selecting good initial guess for SM and inaccuracy of PSO due to being easily trapped into local optimal. The algorithm has sufficiently displayed the performance of PSO's global searching and SM's accurate local search. Numerical computation results show that the approach has great robust, high convergence rate and precision, it can give satisfactory solutions of nonlinear equations.

关键词： systems of nonlinear equations simplex method particle swarm optimization algorithm

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Study on Passenger Train Stopping Scheme Based on Improved particle swarm optimization algorithm

Study on Passenger Train Stopping Scheme Based on Improved P...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Wang, Shuang Zhao, Peng Qiao, Ke Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

ISBN: (纸本)9781424447541

This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway lines Minimizing the stopping times for all passenger trains, minimizing travel distance of empty trains and minimizing the number of transfer passengers are the three planning objectives of the model For a given travel demand and specified capacity of stops, the model is solved by heuristic algorithm An improved discrete particle swarm optimization (PSO) algorithm is presented to determine the best-compromise train stopping scheme with high effectiveness and stability In the algorithm, a stop based representation is designed, and a new method is used to update the position and velocity of particles In order to keep the particle swarm algorithm from premature stagnation, the simulated annealing algorithm, which has local search ability, is combined with the PSO algorithm to make elaborate search near the optimal solution, then the quality of solutions is improved effectively An empirical study on a given small railway network is conducted to demonstrate the effectiveness of the model and the performance of the algorithm The experimental results show that the hybrid algorithm has great advantages in both success rate and convergence speed compared with other discrete PSO algorithm and genetic algorithm, and an optimal set of stopping schemes can always be generated for a given demand To achieve the best planning outcome, the stopping schemes should be flexibly planned, and not constrained by specific ones as often set by the planner

关键词： high-speed railway lines passenger train stopping scheme multi-objective programming particle swarm optimization algorithm simulated annealing algorithm

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Higher-order Multivariate Markov Chains based on particle swarm optimization algorithm for Air Pollution Forecasting

Higher-order Multivariate Markov Chains based on Particle Sw...

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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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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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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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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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