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

Hossain, Alamgir, Madkour, Ammr, Dahal, Keshav and Zhang, Li (2013) A real-time dynamic optimal guidance scheme using a general regression neural network. Engineering Applications of Artificial Intelligence, 26 (4). pp. 1230-1236. ISSN 0952-1976

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Official URL: http://dx.doi.org/10.1016/j.engappai.2012.10.007


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.

Item Type: Article
Uncontrolled Keywords: optimal guidance algorithms, proportional navigation guidance, genetic algorithm, general regression neural network, computational complexity, real-time solution
Subjects: G600 Software Engineering
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ellen Cole
Date Deposited: 13 Dec 2012 18:00
Last Modified: 13 Oct 2019 00:33
URI: http://nrl.northumbria.ac.uk/id/eprint/10783

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