Dynamic Model Learning Using Genetic Algorithm under Adaptive Model Checking Framework

2006 ◽  
Author(s):  
Zhifeng Lai ◽  
S.c. Cheung ◽  
Yunfei Jiang
Keyword(s):  
Genetic Algorithm ◽  
Model Checking ◽  
Dynamic Model ◽  
Adaptive Model ◽  
Model Learning

scholarly journals Model learning and model checking of SSH implementations

2017 ◽  
Author(s):  
Paul Fiterău-Broştean ◽  
Toon Lenaerts ◽  
Erik Poll ◽  
Joeri de Ruiter ◽  
Frits Vaandrager ◽  
...  
Keyword(s):  
Model Checking ◽  
Model Learning

Air Suspension Optimal Control Based on the Genetic Algorithm

2011 ◽  
Vol 467-469 ◽  
pp. 1066-1071
Author(s):  
Zhong Xin Li ◽  
Ji Wei Guo ◽  
Ming Hong Gao ◽  
Hong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Dynamic Model ◽  
Control Method ◽  
Control Model ◽  
Weight Matrix ◽  
Object A ◽  
Full Vehicle ◽  
Air Suspension ◽  
Optimal Control Method

Taking the full-vehicle eight-freedom dynamic model of a type of bus as the simulation object , a new optimal control method is introduced. This method is based on the genetic algorithm, and the full-vehicle optimal control model is built in the MatLab. The weight matrix of the optimal control is optimized through the genetic algorithm; then the outcome is compared with the artificially-set optimal control simulation, which shows that the genetic-algorithm based optimal control presents better performance, thereby creating a smoother ride and improving the steering stability of the vehicle.

Sensor Dynamic Modeling Based on LS-SVM and NGA

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Support Vector ◽  
Local Minima ◽  
Highly Nonlinear ◽  
Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

Topology Optimization for Piecewise Dynamic Models Using a Genetic Algorithm

2010 ◽  
Author(s):  
Yi Liu ◽  
Dragan Djurdjanovic
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Optimization Procedure ◽  
Nonlinear Dynamic System ◽  
Local Model ◽  
Model Parameters ◽  
Highly Nonlinear ◽  
Node Connectivity

It has been demonstrated in the previous research that the node connectivity in the graph encoding the topological neighborhood relationships between local models in a piecewise dynamic model may significantly affect the cooperative learning process. It was shown that a graph with a larger connectivity leads to a quicker learning adaption due to more rapidly decaying transients of the estimation of local model parameters. In the same time, it was shown that the accuracy could be degraded by a larger bias in the asymptotic portion of the estimations of local model parameters. The efforts in topology optimization should therefore strive towards a high accuracy of the asymptotic portion of the estimator of local model parameters while simultaneously accelerating the decay of the estimation transients. In this paper, we pursue minimization of the residual sum of squares of a piecewise dynamic model after a predetermined number of training steps. The optimization of inter-model topology is implemented via a genetic algorithm that manipulates adjacency matrices of the graph underlying the piecewise dynamic model. An example of applying the topology optimization procedure on a peicewise linear model of a highly nonlinear dynamic system is provided to show the efficacy of the new method.

An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

2016 ◽  
Vol 169 ◽  
pp. 888-898 ◽  
Author(s):  
S. Blaifi ◽  
S. Moulahoum ◽  
I. Colak ◽  
W. Merrouche
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Model ◽  
Photovoltaic Applications

System identification using a genetic algorithm and its application to internal adaptive model control

2003 ◽  
Vol 142 (4) ◽  
pp. 45-55 ◽  
Author(s):  
Toshiro Kumon ◽  
Tatsuya Suzuki ◽  
Makato Iwasaki ◽  
Motoaki Matsuzaki ◽  
Nobuyuki Matsui ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
System Identification ◽  
Adaptive Model ◽  
Model Control
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