Multi-resolution pattern analysis for neural network

In recent years, there has been considerable interest and activity within the neural network users community in developing effective and efficient training algorithms. One of the important issue here is to avoid the excessive training known as memorizing and connected to this, generalization capability of the network. In this respect the patterns shown to the network should be representative enough so that in the recall phase the network can make satisfactory estimations within the convex regions of the classified patterns. Hence, the presentation describes the wavelet transform approach for the multi-resolution pattern analysis for advanced processing of neural network input patterns yielding enhanced neural network training. The effectiveness of the method is investigated by means of actual power plant data and the outstanding merits of the approach are highlighted. (authors)