A real-time dynamic optimal guidance scheme using a general regression neural network

This paper presents an investigation into the challenges in implementing a hard real-time optimal non-stationary system using general regression neural network (GRNN). This includes investigation into the dynamics of the problem domain, discretisation of the problem domain to reduce the computationa...

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Vydané v:	Engineering applications of artificial intelligence Ročník 26; číslo 4; s. 1230 - 1236
Hlavní autori:	Hossain, M.A., Madkour, A.A.M., Dahal, K.P., Zhang, Li
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.04.2013
Predmet:	Algorithms Computational complexity Dynamics General regression neural network General regression neural networks Genetic algorithm Interception Mathematical models Missiles Optimal guidance algorithms Optimization Proportional navigation guidance Real time Real-time solution Proportional navigation guidance General regression neural network Genetic algorithm Computational complexity Optimal guidance algorithms Real-time solution
ISSN:	0952-1976, 1873-6769
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Abstract	This paper presents an investigation into the challenges in implementing a hard real-time optimal non-stationary system using general regression neural network (GRNN). This includes investigation into the dynamics of the problem domain, discretisation of the problem domain to reduce the computational complexity, parameters selection of the optimization algorithm, convergence guarantee for real-time solution and off-line optimization for real-time solution. In order to demonstrate these challenges, this investigation considers a real-time optimal missile guidance algorithm using GRNN to achieve an accurate interception of the maneuvering targets in three-dimension. Evolutionary Genetic Algorithms (GAs) are used to generate optimal guidance training data set for a large missile defense space to train the GRNN. The Navigation Constant of the Proportional Navigation Guidance and the target position at launching are considered for optimization using GAs. This is achieved by minimizing the miss distance and missile flight time. Finally, the merits of the proposed schemes for real-time accurate interception are presented and discussed through a set of experiments.
AbstractList	This paper presents an investigation into the challenges in implementing a hard real-time optimal non-stationary system using general regression neural network (GRNN). This includes investigation into the dynamics of the problem domain, discretisation of the problem domain to reduce the computational complexity, parameters selection of the optimization algorithm, convergence guarantee for real-time solution and off-line optimization for real-time solution. In order to demonstrate these challenges, this investigation considers a real-time optimal missile guidance algorithm using GRNN to achieve an accurate interception of the maneuvering targets in three-dimension. Evolutionary Genetic Algorithms (GAs) are used to generate optimal guidance training data set for a large missile defense space to train the GRNN. The Navigation Constant of the Proportional Navigation Guidance and the target position at launching are considered for optimization using GAs. This is achieved by minimizing the miss distance and missile flight time. Finally, the merits of the proposed schemes for real-time accurate interception are presented and discussed through a set of experiments.
Author	Hossain, M.A. Dahal, K.P. Madkour, A.A.M. Zhang, Li
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SubjectTerms	Algorithms Computational complexity Dynamics General regression neural network General regression neural networks Genetic algorithm Interception Mathematical models Missiles Optimal guidance algorithms Optimization Proportional navigation guidance Real time Real-time solution
Title	A real-time dynamic optimal guidance scheme using a general regression neural network
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