抄録
Radial basis function networks (RBFN) are widely applied to many practical problems. One of most prominent features in RBFN is the fact that additional learning can be easily made. This means RBFN provide a good performance under a situation in which the environment changes over time such as in finacial problems. This paper suggests a method for active forgetting in additional learning and shows its effectiveness through some portfolio problems.
