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The porous carbon materials derived from biomass have become the most promising candidates for electrochemical energy conversion and storage due to their renewability and sustain ability. Herein, we present a simple activation and carbonization approach to successfully fabricate a novel tamarisk root-based honeycomb-like porous carbon (TRHPC) by using biomass tamarisk roots as carbon precursor and...
In order to cope with the nonlinear and non-Gaussian time series, a RBF-HMM model, which is based on radial basis function (RBF) neural network with the assumption of measurement noise being hidden Markov model (HMM), is proposed in this paper. On the other hand, most of literatures about neural networks suppose that the number of input is invariable. Obviously, this assumption is improper in some...
In order to cope with nonlinear time series, a variable structure radial basis function (RBF) networks model, in which the numbers of basis functions and input order vary over time, is proposed in this paper. Then sequential Monte Carlo (SMC) method is used for time series on-line prediction and corresponding algorithm is developed. At last, the data of weekly price of the shipbuilding steel product...
A novel method of multistep-ahead prediction based on joint probability distribution is proposed in this paper. Firstly, we introduce the basic theory of hidden Markov model (HMM) and sequential Monte Carlo (SMC) method. Secondly, we make the joint multistep-ahead prediction using SMC method in HMM and then develop the corresponding on-line algorithm. At last, the data of monthly national air passengers...
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