关键词: 粗糙集, RBF神经网络, 隐层优化, 免疫算法

Abstract: This paper proposes a kind of RBF neural network prediction model based on hidden layer optimization algorithm, named HLOA-IRBFM. By introducing rough set into the traditional Immune RBF Neural Network Model(IRBFNM), the initial hidden layer can be classified. It offers a reduction algorithm about the redundant and isolated points by defining the hidden layer area density and relative approximation. The new hidden layer space distributes evenly and the sample space can be covered entirely with few hidden nodes, which bridges a gap of over dependence on the parameters selection of IRBFNM. Experimental result proves that prediction performance of HLOA-IRBFM is more stable and accurate than that of IRBFNM.

Key words: rough set, RBF neural network, hidden layer optimization, immunity algorithm