Prediction model with multiple sparse echo state network
SHEN Li-hua
CHEN Ji-hong
ZENG Zhi-gang
DU Bao-rui
JIN Jian
Abstract：Considering the problem that using a single echo state network(ESN)is difficult to describe the data infor-mation adequately,we propose a multiple sparse echo state network prediction model. The optimized combination model of echo state network is achieved by learning the sparse weights of the related ESN and the sparse weights of related basis functions determined by related sample simultaneously. And the proposed model is achieved with no need of determining the kernel functions and the related kernel parameters,which is different from the double sparse relevance vector machine and the other multiple kernel learning models.So the proposed model not only can describe the information of the datasets better but also can avoid the selection procedure of kernel functions and kernel parameters. There is no need of selecting the spectral radius and sparsity of ESN by cross validation in the proposed model and only the interval of spectral radius and sparsity are needed to be determined. The experimental results of two groups of benchmarking data and a group of real-world dataset demonstrate that the proposed model has better prediction performance.
Keywords：echo state networksparseprediction modelrelevance vector machine
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 421-428 )