A class of fast, supervised learning algorithms is presented. Inspired by Albus's CMAC model, the algorithms learn orders of magnitude more rapidly than typical imple(cid:173) mentations of back propagation, while often achieving comparable qualities of generalization. Furthermore, unlike most traditional function approximation methods, the algorithms are well suited for use in real time adaptive signal processing. Unlike simpler adaptive systems, such as linear predictive cod(cid:173) ing, the adaptive linear combiner, and the Kalman filter, the new algorithms are capable of efficiently capturing the structure of complicated non-linear systems. As an illustration, the algorithm is applied to the prediction of a chaotic timeseries.

A variety of approaches to adaptive information processing have been developed by workers in disparate disciplines. These include the large body of literature on approximation and interpolation techniques (curve and surface fitting), the linear, real-time adaptive signal processing systems (such as the adaptive linear combiner and the Kalman filter), and most recently, the reincarnation of nonlinear neural network models such as the multilayer perceptron. Each of these methods has its strengths and weaknesses. The curve and surface fitting techniques are excellent for off-line data analysis, but are typically not formulated with real-time applications in mind. The linear techniques of adaptive signal processing and adaptive control are well-characterized, but are limited to applications for which linear descriptions are appropriate. Finally, neural network learning models such as back propagation have proven extremely versatile at learning a wide variety of nonlinear mappings, but tend to be very slow computationally and are not yet well characterized.

A variety of approaches to adaptive information processing have been developed by workers in disparate disciplines. These include the large body of literature on approximation and interpolation techniques (curve and surface fitting), the linear, real-time adaptive signal processing systems (such as the adaptive linear combiner and the Kalman filter), and most recently, the reincarnation of nonlinear neural network models such as the multilayer perceptron. Each of these methods has its strengths and weaknesses. The curve and surface fitting techniques are excellent for off-line data analysis, but are typically not formulated with real-time applications in mind. The linear techniques of adaptive signal processing and adaptive control are well-characterized, but are limited to applications for which linear descriptions are appropriate. Finally, neural network learning models such as back propagation have proven extremely versatile at learning a wide variety of nonlinear mappings, but tend to be very slow computationally and are not yet well characterized.

A variety of approaches to adaptive information processing have been developed by workers in disparate disciplines. These include the large body of literature on approximation and interpolation techniques (curve and surface fitting), the linear, real-time adaptive signal processing systems (such as the adaptive linear combiner and the Kalman filter), and most recently, the reincarnation of nonlinear neural network models such as the multilayer perceptron. Each of these methods has its strengths and weaknesses. The curve and surface fitting techniques are excellent for off-line data analysis, but are typically not formulated withreal-time applications in mind. The linear techniques of adaptive signal processing and adaptive control are well-characterized, but are limited to applications forwhich linear descriptions are appropriate. Finally, neural network learning models such as back propagation have proven extremely versatile at learning a wide variety of nonlinear mappings, but tend to be very slow computationally and are not yet well characterized.