Abstract
In this paper, considering the decomposition and composition mechanism of the traditional reservoir, an intelligent interconnected network with multiple reservoir computing in series–parallel configuration, called MRC-IIN, is proposed for multivariate time series classification and prediction. Firstly, according to the unsupervised learning of restricted Boltzmann machine and the Pearson correlation of multivariate inputs, the number of 1st column reservoirs of MRC-IIN can be determined, and each row interconnected reservoirs of MRC-IIN can also be determined. Secondly, according to the input autocorrelation of 1st column reservoirs, the number of each row reservoirs of MRC-IIN can be determined, such that the dynamic characteristics of the corresponding input can be fully reflected. Thirdly, through using the advantages of principal component analysis, each reservoir size of MRC-IIN can be determined, such that the feature information of each row reservoir states can be reduced layer by layer, and the most essential feature information can be retained. Fourthly, in order to ensure the stable application of MRC-IIN, a sufficient condition for global echo state property of MRC-IIN is given. Finally, the simulation results show that the MRC-IIN can improve classification and prediction performance.
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