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检索条件"主题词=Particle swarm optimization algorithm"

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Modeling and Forecasting Method Based on Support Vector Regression

Modeling and Forecasting Method Based on Support Vector Regr...

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2nd International Conference on Future Information Technology and Management Engineering

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

ISBN: (纸本)9780769538808

In the predicting financial distress, we know that 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 use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. The proposed PSOA-SVR model can automatically determine the optimal parameters. This model is tested on the prediction of financial distress. Then, we compare 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： financial distress rough set particle swarm optimization algorithm support vector regression prediction

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A Improved Clustering Analysis Method Based on Fuzzy C-Means algorithm by Adding PSO algorithm

A Improved Clustering Analysis Method Based on Fuzzy C-Means...

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7th International Conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Pang, Liang Xiao, Kai Liang, Alei Guan, Haibing Shanghai Jiao Tong Univ Sch Software Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9783642289415;9783642289422

Fuzzy c-means algorithm (FCM) is one of the most widely used clustering methods for modern medical image segmentation applications. However the conventional FCM algorithm has certain possibilities of converging to a local minimum of the objective function, thus lead to undesired segmentation results. To address this issue, an improved FCM which is based on clustering centroids updates with the use of particle swarm optimization (PSO) is proposed in this paper. This algorithm is designed to support multidimensional feature data and be accessible through parallel computation. The experimental results suggest that, compared to the conventional FCM algorithm, the proposed algorithm leads to higher chances of global optimum clustering and is less computationally intensive when large clustering number is needed.

关键词： Fuzzy c-means particle swarm optimization algorithm Image segmentation Clustering swarm intelligence

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A Study on Fault Diagnosis of Rotating Machinery Combined Wavelet Transform with VMD 4

A Study on Fault Diagnosis of Rotating Machinery Combined Wa...

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4th International Conference on Electrical, Automation and Mechanical Engineering (EAME)

作者： Zhou, Huan Wang, Hao Shanghai Univ Elect Power Machinery & Energy Engn Coll Shanghai 200090 Peoples R China

Effective diagnosis of rotating machinery is difficult in view of the complex structure, weak early fault signals, non-stationary and non-linear vibration signals, and low signal-to-noise ratio. In this paper, a fault diagnosis method is proposed based on particle swarm optimization (PSO) and variational modal decomposition (VMD). Firstly, wavelet packet threshold is denoised on the signal, VMD is decomposed on the reconstructed signal, and PSO is optimized on the inherent mode function (IMF) obtained from decomposition so as to determine the best IMF function. Then Hilbert transform and envelope spectrum analysis are carried out on the IMF function, and the envelope spectrum analysis result is compared with theoretical calculation frequency to finally determine the fault type. The results indicate that this method can effectively reduce noise components in signals, extract weak fault information and realize fault diagnosis.

关键词： Wavelet packet denoising VMD IMF particle swarm optimization algorithm Rotating machinery

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optimization and CONTROL of a HEV

Optimization and CONTROL of a HEV

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International Conference on Electrical, Electronics, and optimization Techniques (ICEEOT)

作者： Farahat, Mohammad Esmaeil Farahat, Said Islamic Azad Univ Aliabad Katoul Branch Dept Elect Engn Aliabad Katoul Iran Univ Sistan & Baluchestan Dept Mech Engn Zahedan Iran

ISBN: (纸本)9781467399395

Vehicles play an important role in transportation. Using fossil fuels as their source of energy, they are a source of pollution. HEVs are improving and a day will come that they will take over the role of conventional vehicles. In the path of improving HEVs, optimizing components sizes of the vehicle and better strategy controls are vital methods to reach the desired goals. In this path practical tools for optimization are evolutionary algorithms and fuzzy controllers as a strategy control decision maker. In this paper, accompanied with these tools extraordinary improvements are made. On the UDDS cycle using PSO algorithm we reached 57%, 68%, 34% and 8% decrease respectively in fuel consumption, emission, economic and acceleration and we reached 30% increase in maximum speed gained. On the HWFET cycle we reached 74%, 79%, 34% and 8% decrease respectively in fuel consumption, emission, economic and acceleration and we reached 30% increase in maximum speed gained. Acquiring a Pareto Front for two fitness functions we can choose a specific vehicle with special capabilities according to our priorities.

关键词： Hybrid vehicles particle swarm optimization algorithm power management strategy sizing drive cycle

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Determining PEMFC Model Parameters with IPSO algorithm

Determining PEMFC Model Parameters with IPSO Algorithm

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2nd International Conference on Energy, Environment and Sustainable Development (EESD 2012)

作者： Wang, Xiaodong Ye, Meiying Xu, Yousheng Zhejiang Normal Univ Dept Elect Engn Jinhua 321004 Peoples R China Zhejiang Normal Univ Dept Phys Jinhua 321004 Peoples R China

ISBN: (纸本)9783037855492

An improved particle swarm optimization algorithm is proposed for determining proton exchange membrane fuel cell (PEMFC) model parameters according to its V-I characteristics. In the algorithm, the weight update is adaptive with the change of objective function. The test results indicate that satisfying parameter accuracy can be achieved by the algorithm. Also, the V-I characteristics obtained by the improved particle swarm optimization algorithm are in good agreement with the simulated data.

关键词： PEMFC Model Parameter Determination particle swarm optimization algorithm

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Optimal Design of Power System stabilizer in a Multi-machine System Using Evolutionary Computing, A Comparative Study

Optimal Design of Power System stabilizer in a Multi-machine...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Nayak, Niranjan Pradhan, Soumya Ray, Sangram Keshari Rout Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EICE Bhubaneswar Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EE Bhubaneswar Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EEE Bhubaneswar Odisha India

ISBN: (纸本)9781479974559

When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is because, the PSS is low cost, flexible and easy to implement. In this work the problem is designed as a single objective optimization technique to tune the PSS parameters with Eigenvalue analysis. Here the Invasive Weed optimization algorithm which is found suitable for these types of problems is selected as a tool to find optimal solutions. Simulations are executed on a 4-machine power system for different operating conditions such as heavy load, light load and capacitive load. The results are obtained under different fault conditions with Invasive Weed optimization technique and compared the same with PSO. At last it is observed that the IWO technique performs better in damping overshoot and settling time.

关键词： particle swarm optimization algorithm Invasive weed optimization technique Low frequency oscillation Stability of Power system

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Comparative Analysis of Different Prediction Models of Inorganic Fertilizers N, P, and K Under Different Soil Fertility Conditions 36

Comparative Analysis of Different Prediction Models of Inorg...

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36th Chinese Control and Decision Conference (CCDC)

作者： Luo, Yuzhu Yu, Xiaoyu Zhou, Yue Bai, Bing Dong, Huan Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China Liaoning Acad Agr Sci Inst Plant Nutr & Environm Resources Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Nutrient management is a key measure to achieve the target crop yield. Choosing appropriate predictive models and adjusting nutrient supply according to actual conditions can effectively reduce nutrient loss, improve crop yield and product quality. Therefore, we conducted research on the prediction models of inorganic fertilizers N, P, and K in greenhouse tomatoes based on the target yield. In this paper, we analyzed four different neural network prediction models: the neural network prediction model based on ant, colony optimization algorithm, the neural network prediction model based on sparrow search algorithm, neural network prediction model based on genetic algorithm and neural network prediction model based on particle swarm optimization algorithm, and compared and analyzed the prediction results of inorganic fertilizer N, P and K under different soil fertility conditions to understand which neural network prediction model will produce the best prediction effect. The simulation results showed that under medium soil fertility, the neural network prediction model based on genetic algorithm had the best prediction effect of inorganic fertilizer N, P, and K. The verification results showed that its mean square error (MSE) and coefficient of determination (R-2) were the best, which were 0.0031 and 0.8200 respectively. However, under low soil fertility and high soil fertility, the neural network prediction model based on the sparrow search algorithm had the best prediction performance, and its MSE and R-2 were the best.

关键词： ant colony optimization algorithm sparrow search algorithm genetic algorithm particle swarm optimization algorithm inorganic fertilizer prediction

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The Influence of the Solar Flywheel Energy Storage Control System Based on the Improved Disturbance Observation Method on the Improvement of New Energy Efficiency 5

The Influence of the Solar Flywheel Energy Storage Control S...

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5th International Conference on Environmental Science and Material Application (ESMA)

作者： Zhu, Leyu North China Elect Power Univ Baoding Baoding Hebei Peoples R China

The purpose of this study was to improve the utilization efficiency of solar energy, and accelerate the innovation and development of solar flywheel energy storage control system, so as to enhance the utilization value of new energy. Firstly, the definition and working method of flywheel energy storage control system were described, and the characteristics of flywheel energy storage control system were introduced in detail. Secondly, the concept and working principle of solar flywheel energy storage control system were introduced, and the principle and calculation method of perturbation and observation method (P&O) were described in detail. Finally, the improved P&O based on particle swarm optimization (PSO) algorithm was described in detail, and its principle and operation methods were explained. The research results show that the biggest characteristic of flywheel energy storage device is that it has no pollution to the environment, the energy stored per unit area is large, and the efficiency of converting it into electric energy is very high, which can be used for a long time. The working property of solar energy storage system is the introduction, maintenance, and output of electric energy. In order to obtain the best tracking data, the flywheel energy storage control system must control the disturbance amplitude and time accurately and make a comprehensive choice. The P&O improved by PSO algorithm is applied to the flywheel energy storage control system, and the experience time to reach the maximum efficiency position is relatively shortened. When the system's derived power reaches the balance, the variation of wave shape in the improved dynamic observation method is also relatively reduced.

关键词： Solar flywheel energy storage control system particle swarm optimization algorithm disturbance observation method new energy

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach 34

The State of Charge Estimation of Lithium-ion Batteries Usin...

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34th Chinese Control and Decision Conference (CCDC)

作者： He, Wei Ma, Hongyan Zhang, Yingda Wang, Shuai Dou, Jiaming Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Natl Virtual Simulat Expt Ctr Smart City Educ Beijing 100044 Peoples R China

ISBN: (纸本)9781665478960

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. In order to improve the estimation accuracy of SOC, this paper develops a SOC estimation model for a lithium-ion battery using a particle swarm optimization-Extreme Learning Machine(PSO-ELM) algorithm. The PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM model. The inputs to the PSO-ELM model are the battery voltage, current, and temperature, and the output is the actual SOC values. The performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge particle swarm optimization algorithm Extreme Learning Machine

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Soft Sensor Modeling Based on PSO-FNN for Lysine Fermentation Process

Soft Sensor Modeling Based on PSO-FNN for Lysine Fermentatio...

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2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)

作者： Huang, Yonghong Xia, Chenglin Sun, Yukun Zhu, Xianglin Wang, Yuejun Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Peoples R China Chinese Acad Fishery Sci Yellow Sea Fisheries Res Inst Qingdao Peoples R China

ISBN: (纸本)9781424451944

In fermentation process, fuzzy neural networks (FNN) is a novel machine learning method of soft sensor modeling, while the typical algorithm of FNN is inefficient because they can not optimize fuzzy rules and has long training time. Biological parameters can be measured online in real time which is helpful for the control of process optimization. So this paper introduces the use of the particle swarm optimization (PSO) for training FNN. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using PSO. The PSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Experiment results show that, in contrast to the traditional fuzzy neural networks, the method has good prediction and is suitable to practical applications.

关键词： particle swarm optimization algorithm fuzzy neural networks soft sensor modeling

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