| || Missile Defence and Interceptor Allocation by LVQ-RBF Multi-agent Hybrid Architecture
Author : Thamarai Selvi, S.;Malmathanraj, R.
Source : Defence Science Journal ; Vol:57(2) ; 2007 ; pp 173-183
Subject : 629.7 Aeronautics
Keywords : Reinforcement learning;LVQ neural network;Theatre missile defence;Interceptor allocation;Q-learning;RBF neural network;Radial basis function;Learning vector quantisation
Abstract : This paper proposes a solution methodology for a missile defence problem using theatre missile defence (TMD) concept. In the missile defence scenario, the concept of TMD is generally used for the optimal allocation of interceptors to counter the attack missiles. The problem is computationally complex due to the presence of enormous state space. The Learning vector quantiser–Radial basis function (LVQ-RBF) multi-agent hybrid neural architecture is used as the learning structure, and Q-learning as the learning method. The LVQ-RBF multi-agent hybrid neural architecture overcomes the complex state space issue using the partitioning and weighted learning approach. The proposed LVQ-RBF multi- agent hybrid architecture improvises the learning performance by the local and global error criterion. The state space is explored with initial coarse partitioning by LVQ neural network. The fine partitioning of the state space is performed using the multi-agent RBF neural network. The discrete reward scheme is used for LVQ-RBF multi-agent hybrid neural architecture. It has a hierarchical architecture which enables quicker convergence without the loss of accuracy. The simulation of the TMD is performed with 500 assets and six priority of assets.