RBF learning in a non-stationary environment : the stability-plasticity dilemma

Abstract

This chapter focuses on learning with RBF networks in a nonstationary environment. A non-stationary environment demands a neural network to continuously learn. More difficult than following the change is the ability of learning new patterns without forgetting old prototype patterns, also termed as the stability-plasticity dilemma. A local representation and… (More)

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