SUMMARYIn this study, the authors propose an HMRAN (Hyper MRAN) for which dimension selection is possible, by extending a minimal resource allocating network (MRAN) for online learning with minimized resources, with the application of a growth strategy and pruning strategy for hidden units in a Gaussian radial basis function (GRBF) network. When the input is a multidimensional pattern, it is often the case that dimensions with an explicit relationship to the output are limited to a single section and it is difficult to develop a correspondence with a MRAN having an isotropic Gaussian function as the basis function. Dimension selection can be flexibly performed in basis function units with the use of a hyper basis function (Hyper BF) instead of a Gaussian function. Furthermore, computational complexity is reduced by using a localized extended Kalman filter (LEKF) and the introduction of a merging strategy for the basis function and improvements to the pruning strategy resulted in a reduction of the resources needed. A number of benchmark tests were performed and it was confirmed that the proposed technique can be implemented with good accuracy and minimal resources compared to the conventional technique.

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