出版社：Defence Scientific Information & Documentation Centre
摘要：This paper proposes and implements a neural architecture to solve the weapon allocation problem in the multi-layer defense scenario using modified BIONET neural network architecture. The presynaptic layer of the modified BIONET reduces the dimensionality of the principal state equation by partitioning the state space. The post-synaptic layer of the modified BIONET includes the perceptron Q-learning rule. The cortical layer incorporates L-learning scheme to provide better exploration over action space. Thus, action selection is effectively made with quicker convergence of training. The reward scheme in the reinforcement learning is obtained by calculating the measure of probability of survival. The decision module has been enhanced by incorporating the features corresponding to the battle weapons for effective representation of the environment. Thus, the modified BIONET neural architecture is used to increase the efficiency of assets saved in the simulation and the time complexity is reduced due to the state-space partitioning scheme involved in the neural network. The proposed modified BIONET is implemented in MATLAB and the percentage of assets saved is increased. Also, the training time is drastically reduced. Thus, the modified BIONET resulted in saving more assets with faster convergence of learning.
