Adaptive learning networks in APL2

Abstract

The paper considers Adaptive Learning Networks (ALN) as a tool to solve the problems of modeling, prediction, diagnostics and pattern recognition in complex systems. This method is similar to the neural network technique. The main difference is the self-organization of network structure on the basis of generation and estimation of various nodes, connections and weights. A set of functions presented in the paper shows that ALNs are easily realized in APL2. User-defined operators are used as a very convenient tool for ALN programming. The paper discusses the application of implemented software to the problem of Burnout Heat Flux Prediction in nuclear reactors. It is shown that ALN technique allows the prediction of burnout heat flux with approximately three times better accuracy than other commonly used methods.