Enhancements to the dynamic self-organising neural network

Abstract

Class Directed Unsupervised Learning (CDUL) is a dynamic self-organising network which has been shown to overcome many of the problems associated with unsupervised learning, thereby yielding performance characteristics superior to similar networks such as counter-propagation and LVQ. In this paper, the CDUL algorithm is developed further, to a point where the original two-phase learning process is combined into a single system of dynamic parameter variation; a training cycle that can then be terminated automatically at a point of zero error over the training set. The ability to improve training times using a FastCDUL algorithm is also explored. The new algorithm, CDUL2, is subsequently applied to the benchmark problem of mine detection given sonar data, and shown to outperform both backpropagation and LVQ in terms of training speed and recall performance. Finally, a measure of computational cost is estimated for both CDUL2 and LVQ training periods, reinforcing the suggested efficiency of CDUL2 over its counterparts.