Structural adaptation in neural networks with application to land mine detection

This paper presents a new approach for structural adaptation in multilayer neural networks in general and the application of the proposed method to land mine target detection and classification problem. The new algorithm uses time and order update formulations of the orthogonal projection theorem to derive a recursive weight updating procedure and architectural variation of the network during the training process. The proposed approach provides optimal network structure in the sense that the mean-squared error is minimized for the newly created topology. This algorithm is used in conjunction with a data representation scheme to perform land mine target detection and classification. The simulation results on targets with different compositions indicated superior detection and classification performance when compared to the conventional methods.