scholarly journals Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm

2021 ◽  
Vol 15 (2) ◽  
pp. 342-353
Author(s):  
Chu Zhang ◽  
Tian Peng ◽  
Jianzhong Zhou ◽  
Muhammad Shahzad Nazir
Keyword(s):  
Differential Evolution ◽  
Parameter Identification ◽  
Uncertainty Quantification ◽  
Metropolis Algorithm ◽  
Pump Turbine ◽  
Governing System ◽  
Non Linear

scholarly journals Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1668 ◽  
Author(s):  
Jianzhong Zhou ◽  
Chu Zhang ◽  
Tian Peng ◽  
Yanhe Xu
Keyword(s):  
Parameter Identification ◽  
Search Algorithm ◽  
Pump Turbine ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Governing System

Parameter identification of turbine speed governing system by using genetic algorithm

2012 ◽  
Author(s):  
Tian Liwei ◽  
Xiao Xianyong ◽  
Li Changsong ◽  
Zhou Hongjun
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Governing System ◽  
Turbine Speed

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

scholarly journals Takagi–Sugeno fuzzy generalised predictive control of a time‐delay non‐linear hydro‐turbine governing system

2019 ◽  
Vol 13 (13) ◽  
pp. 2338-2345 ◽  
Author(s):  
Yuqiang Tian ◽  
Bin Wang ◽  
Delan Zhu ◽  
Fengjiao Wu
Keyword(s):  
Time Delay ◽  
Predictive Control ◽  
Hydro Turbine ◽  
Governing System ◽  
Non Linear ◽  
Takagi Sugeno

Generalized Differential Evolution for General Non-Linear Optimization

COMPSTAT 2008 ◽  
2008 ◽  
pp. 459-471 ◽  
Author(s):  
Saku Kukkonen ◽  
Jouni Lampinen
Keyword(s):  
Differential Evolution ◽  
Linear Optimization ◽  
Non Linear ◽  
Generalized Differential

Comparative Study of Evolutionary Algorithms for Parameter Identification of an Impact Oscillator

2014 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu Mahfouz
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Optimization Techniques ◽  
System Response ◽  
Impact Oscillator ◽  
Swarm Optimization ◽  
System Parameters ◽  
Highly Nonlinear ◽  
The Impact

The use of non-classical evolutionary optimization techniques such as genetic algorithms, differential evolution, swarm optimization and genetic programming to solve the inverse problem of parameter identification of dynamical systems leading to chaotic states has been gaining popularity in recent years. In this paper, three popular evolutionary algorithms — differential evolution, particle swarm optimization and the firefly algorithm are used for parameter identification of a clearance-coupled-impact oscillator system. The behavior of impacting systems is highly nonlinear exhibiting a myriad of harmonic, low order and high order sub-harmonic resonances, as well as chaotic vibrations. The time-history simulations of the single-degree-of-freedom impact oscillator were obtained by the Neumark-β numerical integration algorithm. The results are illustrated by bifurcation graphs, state space portraits and Poincare’ maps which gives valuable insights on the dynamics of the impact system. The parameter identification problem relates to finding one set of system parameters given a chaotic or periodic system response as a set of Poincaré points and a different but known set of system parameters. The three evolutionary algorithms are compared over a set of parameter identification problems. The algorithms are compared based on solution quality to evaluate the efficacy of using one algorithm over another.

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