Medical sample classifier design using fuzzy cerebellar model neural networks

Abstract

This study designs a stable and convergent adaptive fuzzy cerebellar model articulation classifier for the identification of medical samples. Research on medical diagnosis is important and complex. Uncertainty is unavoidable in medical diagnosis. The symptoms are one of the uncertainties of an illness and may or may not occur as a result of the disease. There is an uncertain relationship between symptoms and disease. Diagnostic accuracy can be improved using data forecasting techniques. This study proposes a generalized fuzzy neural network, called a fuzzy cerebellar model articulation controller (FCMAC). It is an expansion of a fuzzy neural network and it will be shown that the traditional fuzzy neural network controller is a special case of this FCMAC. This expansion type fuzzy neural network has a greater ability to be generalized, has greater learning ability and a greater capacity to approximate than a traditional fuzzy neural network. The steepest descent method has a fast convergent characteristic, so it is used to derive the adaptive law for the adaptive fuzzy cerebellar model classifier. The optimal learning rate is also derived to achieve the fastest convergence for the FCMAC, in order to allow fast and accurate identification. The simulation results demonstrate the effectiveness of the proposed FCMAC classifier.