The modified probabilistic neural network was initially derived from Specht's (1990) probabilistic neural network classifier and developed for nonlinear time series analysis. It can be described as a vector quantised reduced form of Specht's general regression neural network. It is typically trained with a known set of representative data pairs. This is quite satisfactory for stationary data statistics, but for the nonstationary case it is necessary to be able to adapt the network during operation. This paper describes adaptive learning schemes for the modified probabilistic neural network for both stationary and nonstationary data statistics. A nonlinear control problem is used to illustrate and compare the network's learning ability with that of the general regression and radial basis function neural networks.
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