Abstract
A self-adaptive quantum radial basis function network (QRBFN) is proposed for classification applications. The QRBFN model is a three-layer structure. The hidden layer of the QRBFN model contains quantum function neurons, which are multilevel activation functions. Each quantum function neuron is composed of the sum of sigmoid functions shifted by quantum intervals. A self-adaptive learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is proposed. The proposed the SCA method is a fast, one-pass algorithm for a dynamic estimation of the number of clusters in an input data space. The backpropagation algorithm is used to tune the adjustable parameters. Simulation results were conducted to show the performance and applicability of the proposed model.
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