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A Neural Genetic Hybrid Model for Eigenstructure Allocation in the LQR Project in DFIG

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Ivanildo Abreu, Rildenir Silva, Luan Pereira

Ivanildo Abreu, Rildenir Silva and Luan Pereira. A Neural Genetic Hybrid Model for Eigenstructure Allocation in the LQR Project in DFIG. International Journal of Computer Applications 162(12):9-15, March 2017. BibTeX

	author = {Ivanildo Abreu and Rildenir Silva and Luan Pereira},
	title = {A Neural Genetic Hybrid Model for Eigenstructure Allocation in the LQR Project in DFIG},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {12},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {9-15},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017913419},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


A hybrid neuronal genetic model is proposed with the objective of solving the Riccati Algebraic Equation (RAE) that is associated to the restricted optimization structure of the Linear Quadratic Regulator (LQR) problem. The application of this hybrid model of artificial intelligence will be performed in a wind power generation system, in particular, the double fed induction generator (DFIG). For this, a recurrent neural network with multiple layers is used where its performance is realized by metrics of the norm of infinity associated with RAE and energy surfaces as a function of the positive definite symmetric matrix and the Cholesky factor.


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Artificial Neural Networks (RNA), genetic algorithm (GA), DFIG, LQR.