Model learning from weights by adaptive enhanced probabilistic convergent network

Current weightless classifiers require historical data to model a system and make prediction about a system successfully. Historical data either is not always available or does not take a recent system modification into consideration. For this reason an adaptive filter is designed, which when employed with a weightless classifier enables system model, difficult to characterise system model, and system output prediction, successfully. Results of experiments performed show that the fusion of an adaptive filter and a weightless classifier is more beneficial than the filter or the classifier utilised singly, and that no speed advantage is observed.