Object oriented software quality prediction using general regression neural networks
ACM SIGSOFT Software Engineering Notes
Reliability and Performance Optimization of Pipelined Real-Time Systems
ICPP '10 Proceedings of the 2010 39th International Conference on Parallel Processing
Intelligent Learning Algorithms for Active Vibration Control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A general regression neural network
IEEE Transactions on Neural Networks
Differential evolution based 3-D guidance law for a realistic interceptor model
Applied Soft Computing
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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.