A novel learning law for a single neuron

In this paper, the learning gain is reconsidered from the viewpoint of the adaptive control systems. We present a novel learning law for a single neuron, which is a kind of /spl sigma/-modified adaptive law used in the robust adaptive control systems. We presents a brief proof of boundedness of the estimator to be learned and a simple numerical simulation, where we show the viability of the proposed learning law.<>