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

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    Publication date
    2013
    Author
    Hossain, M. Alamgir
    Madkour, A.A.M.
    Dahal, Keshav P.
    Zhang, L.
    Keyword
    Optimal guidance algorithms
    ; Proportional navigation guidance
    ; Genetic algorithm
    ; General regression neural network
    ; Computational complexity
    ; Real-time solution
    Peer-Reviewed
    Yes
    
    Metadata
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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. (C) 2012 Elsevier Ltd. All rights reserved.
    URI
    http://hdl.handle.net/10454/9580
    Version
    No full-text available in the repository
    Citation
    Hossain A, Madkour AMM, Dahal K et al (2013) A real-time dynamic optimal guidance scheme using a general regression network. Engineering Applications of Artificial Intelligence. 26(4): 1230-1236.
    Link to publisher’s version
    http://dx.doi.org/10.1016/j.engappai.2012.10.007
    Type
    Article
    Collections
    Engineering and Informatics Publications

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