A New Fast Training Algorithm of the WTM Kohonen Neural Network Implemented for Classification of Biomedical Signals

A new optimized algorithm for the learning process suitable for hardware implemented Winner Takes Most Kohonen Neural Network (KNN) has been proposed in the paper. In networks of this type a neighborhood mechanism is used to improve the convergence properties of the network by decreasing the quantization error. The proposed technique bases on the observation that the quantization error does not decrease monotonically during the learning process but there are some ‘activity’ phases, in which this error decreases very fast and then the ‘stagnation’ phases, in which the error does not decrease. The stagnation phases usually are much longer than the activity phases, which in practice means that the network makes a progress in training only in short periods of the learning process. The proposed technique using a set of linear and nonlinear filters detects the activity phases and controls the neighborhood R in such a way to shorten the stagnation phases. As a result, the learning process may be 16 times faster than in the classic approach, in which the radius R decreases linearly. The intended application of the proposed solution will be in Wireless Body Sensor Networks (WBSN) in classification and analysis of the EMG and the ECG biomedical signals.