关键词: 可拓聚类, 极限学习机, 径向基函数, 回归, 分类

Abstract: During the construction process of Extreme Learning Machine (ELM), its input weights are randomly generated, and these parameters are non-optimized and contain no prior knowledge of the inputs. To solve these problems, combining the clustering method of Extension Neural Network type 2 (ENN-2), an extension clustering based extreme learning machine (EC-ELM) neural network was proposed. In EC-ELM neural network, the radial basis function centers of hidden neurons were firstly taken as the input weights, then extension clustering method was used to adaptively adjust the hidden neurons number and center vectors, and this well-adjusted information was trained by Moore-Penrose generalized inverse to obtain the output weights. Meanwhile, the effectiveness of this network was tested by the Friedman#1 dataset and the Wine dataset. The results indicate that EC-ELM provides a simple and convenient way to train the structure and parameters of neural network, and it is of higher modeling accuracy and faster learning speed than Extension theory based Radial Basis Function (ERBF) or ELM, which will provide a new way to apply the EC-ELM to complex process modeling.

Key words: extension clustering, extreme learning machine, radial basis function, regression, classification