TY - JOUR
T1 - A real-time dynamic optimal guidance scheme using a general regression neural network
AU - Hossain, Alamgir
AU - Madkour, Ammr
AU - Dahal, Keshav
AU - Zhang, Li
PY - 2013/4
Y1 - 2013/4
N2 - 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.
AB - 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.
KW - optimal guidance algorithms
KW - proportional navigation guidance
KW - genetic algorithm
KW - general regression neural network
KW - computational complexity
KW - real-time solution
U2 - 10.1016/j.engappai.2012.10.007
DO - 10.1016/j.engappai.2012.10.007
M3 - Article
VL - 26
SP - 1230
EP - 1236
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
SN - 0952-1976
IS - 4
ER -