A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

Wavelet Neural Network Model for Short-Term Wind Power Forecasting Based on Particle Swarm Optimization Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 806-812 ◽  
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important to the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network is proposed. At the same time in order to overcome the disadvantages of the wavelet neural network for only use error reverse transmission as a fixed rule, this paper puts forward using Particle Swarm Optimization algorithm to replace the traditional gradient descent method training wavelet neural network. Through the analysis of the measured data of a wind farm, Shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

MODELING OF INPUT-OUTPUT RELATIONSHIPS FOR ELECTRON BEAM BUTT WELDING OF DISSIMILAR MATERIALS USING NEURAL NETWORKS

2014 ◽  
Vol 13 (03) ◽  
pp. 1450016 ◽  
Author(s):  
M. N. JHA ◽  
D. K. PRATIHAR ◽  
A. V. BAPAT ◽  
V. DEY ◽  
MAAJID ALI ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Electron Beam ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Input Output ◽  
Swarm Optimization ◽  
Butt Welding

Electron beam butt welding of stainless steel (SS 304) and electrolytically tough pitched (ETP) copper plates was carried out according to central composite design of experiments. Three input parameters, namely accelerating voltage, beam current and weld speed were considered in the butt welding experiments of dissimilar metals. The weld-bead parameters, such as bead width and depth of penetration, and weld strength in terms of yield strength and ultimate tensile strength were measured as the responses of the process. Input-output relationships were established in the forward direction using regression analysis, back-propagation neural network (BPNN), genetic algorithm-tuned neural network (GANN) and particle swarm optimization algorithm-tuned neural network (PSONN). Reverse mapping of this process was also conducted using the BPNN, GANN and PSONN approaches, although the same could not be done from the obtained regression equations. Neural networks were found to tackle the problems of both forward and reverse mappings efficiently. However, neural networks tuned by the genetic algorithm and particle swarm optimization algorithm were seen to perform better than the BPNN in most of the cases but not all.

scholarly journals Application of Neural Network Integration in Fault Diagnosis

2014 ◽  
Vol 8 (1) ◽  
pp. 81-84
Author(s):  
Wei Xiong ◽  
Xuehui Xian ◽  
Lijing Zhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Method ◽  
Network Integration ◽  
Swarm Optimization ◽  
Integration System ◽  
Simulation Results ◽  
The Individual

According to the generation methods of individual neural network and the methods of generating conclusions from integrated neural network, an effective neural network integration system can be constructed. An optimization method for neural network integration is proposed. In the generation of individuals in the network integration, a variety of genetic algorithms and particle swarm optimization algorithm are used to train individual networks, thus to improve the precision of network members and reduce the correlation among the network members; in the conclusion generation, weight of the individual neural network is dynamically determined. The simulation results show that the effectiveness and feasibility of the method in fault diagnosis.

An Improved Nonlinear Flight Controller Design Using Active Disturbances Rejection Control Method

2013 ◽  
Vol 380-384 ◽  
pp. 332-336
Author(s):  
Zhan Qi Fan ◽  
Lin Liu ◽  
Xun Sun
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Particle Swarm Optimization Algorithm ◽  
Control Method ◽  
Controller Design ◽  
Wavelet Neural Network ◽  
Control Performance ◽  
Swarm Optimization ◽  
Excellent Control ◽  
And Control

An improved large envelope nonlinear flight control method using active disturbances rejection control (ADRC) method and wavelet neural network is approved in this paper. Wavelet neural network is used to realize the inversion of the 6-DOF nonlinear airplane model. The wavelet neural network is optimized using simulated annealing particle swarm optimization algorithm to improve the approach precision. In order to improve the robustness and control performance in all disturbances, ADRC is used to realize the high precision flight control. The simulation results show that the large envelope flight controller has excellent control performance.

scholarly journals A Novel Approach for Shearer Cutting Load Identification through Integration of Improved Particle Swarm Optimization and Wavelet Neural Network

2014 ◽  
Vol 6 ◽  
pp. 521629 ◽  
Author(s):  
Zhongbin Wang ◽  
Lei Si ◽  
Chao Tan ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Wavelet Neural Network ◽  
Population Diversity ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Novel Approach ◽  
Comparison Results

In order to accurately identify the change of shearer cutting load, a novel approach was proposed through integration of improved particle swarm optimization and wavelet neural network. An improved updating strategy for inertia weight was presented to avoid falling into the local optimum. Moreover, immune mechanism was applied in the proposed approach to enhance the population diversity and improve the quality of solution, and the flowchart of the proposed approach was designed. Furthermore, a simulation example was carried out and comparison results indicated that the proposed approach was feasible, efficient, and outperforming others. Finally, an industrial application example of coal mining face was demonstrated to specify the effect of the proposed system.

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