INTEGRATED UNSUPERVISED AND ERROR-DRIVEN LEARNING FOR INCREMENTAL PROCESS IDENTIFICATION WITH NEURAL SINGLETON MODELS
Gancho L. Vachkov · International Journal of Computational Intelligence and Applications · 2004
In the paper a special incremental identification procedure for learning a Neural Singleton (NS) Model, as a simplification of the RBFNN model is proposed. At each epoch of this incremental procedure a special integrated learning algorithm for the neuron centers and then a supervised learning for the singletons are performed. As a result, the approximation error of the NS model is gradually decreased with the increment of the neurons at each learning epoch. The newly proposed integrated unsupervised and Error-Driven learning algorithm is the main part of the incremental identification procedure. It is a modified version of the Neural-Gas learning algorithm, which uses the normalised evaluation error as feedback information. Thus the integrated learning algorithm is able to drive the neurons to the "more interesting" areas in the input space, where bigger evaluation errors exist, instead of just locating them in the areas of higher data density. The final result is an incremental growing type of NS model that gradually improves its approximation accuracy at each learning epoch. A detailed test example is shown in the paper in order to evaluate the performance and to show the important features of the whole incremental identification procedure, including the integrated learning algorithm. Comparison results with the conventional (one-epoch and non-incremental) model are also given.