Hydro Turbine Nonlinear Model Parameter Identification Based on Improved Biogeography-Based Optimization

2014 ◽  
Vol 672-674 ◽  
pp. 1617-1621
Author(s):  
Jie Zhao ◽  
Li Wang ◽  
Yu Heng Tang ◽  
Di Chen Liu ◽  
Wen Tao Sun
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Model ◽  
Measured Data ◽  
Operating Efficiency ◽  
Swarm Optimization ◽  
Identification Methods ◽  
Species Migration ◽  
Hydro Turbine ◽  
Data Comparison ◽  
Improve Operating

The nonlinear model of hydro turbine considering the elastic water column was proposed, and the improved biogeography-based optimization (IBBO) algorithm was applied to identify parameters of this model. The cosine species migration model and the elitist reservation strategy were introduced into the algorithm to improve operating efficiency. Based on measured data, comparison of the effects of different identification methods was presented, including the IBBO algorithm, genetic algorithm (GA) and particle swarm optimization (PSO). The results demonstrated that the IBBO algorithm can be applied in parameters identification of hydro turbine nonlinear model, and it has the advantages of faster convergence and higher precision.

Optimization of a nonlinear model for predicting the ground vibration using the combinational particle swarm optimization-genetic algorithm

2017 ◽  
Vol 133 ◽  
pp. 36-45 ◽  
Author(s):  
Hossein Samareh ◽  
Seyed Hassan Khoshrou ◽  
Kourosh Shahriar ◽  
Mohammad Mehdi Ebadzadeh ◽  
Mohammad Eslami
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nonlinear Model ◽  
Particle Swarm ◽  
Ground Vibration ◽  
Swarm Optimization

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.

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