著者

斉藤 幹貴 榎本 剛 松本 隆

出版者

一般社団法人 システム制御情報学会

雑誌

システム制御情報学会論文誌 (ISSN:13425668)

巻号頁・発行日

vol.15, no.1, pp.10-16, 2002-01-15

参考文献数

7

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A hierarchical Bayesian approach is formulated for nonlinear time series prediction problems with neural nets. The proposed scheme consists of several steps : <BR>(i) Formulae for posterior distributions of parameters, hyper parameters as well as models via Bayes formula.<BR>(ii) Derivation of predictive distributions of future values taking into account model marginal likelihoods.<BR>(iii) Using several drastic approximations for computing predictive mean of time series incorporating model marginal likelihoods.<BR>The proposed scheme is tested against two examples; (A) Time series data generated by noisy chaotic dynamical system, and (B) Building air-conditioning load prediction problem. The proposed scheme outperforms the algorithm previously used by the authors.