基于貝葉斯神經(jīng)網(wǎng)絡(luò)方法的短期負(fù)荷預(yù)測-資料下載頁
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【正文】 ern recognition. Oxford: Oxford University Press。 1995.[7] Reed R. Pruning algorithms – a survey. IEEE Trans Neural Networks 1996。4(5):740–7.[8] Lauret P, Fock E, Mara TA. A node pruning algorithm based on the Fourier amplitude sensitivity test method. IEEE Trans Neural Networks 2006。17(2):273–93.[9] Yuan JL, Fine TL. Neuralnetwork design for small training sets of high dimension. IEEE Trans Neural Networks 1998。9(2): 267–80.[10] Alves da Silva AP, Moulin LS. Confidence intervals for neural network based shortterm load forecasting. IEEE Trans Power Syst 2000。15(4):1191–6.[11] MacKay DJC. Information theory, inference, and learning algorithms. Cambridge: Cambridge University Press。 2003.[12] Malakoff DM. Bayes offer ‘new’way to make sense of numbers. Science 1999。286:1460–4.[13] Cox RT. Probability, frequency and reasonable expectation. Am J Phys 1946。14:1–13.[14] Sivia DS. Data analysis: a Bayesian tutorial. Oxford: Oxford University Press。 1996.[15] Jaynes ET. Probability theory – the logic of science. Cambridge: Cambridge University Press。 2003.[16] MacKay DJC. A practical Bayesian framework for backpropagation networks. Neural Comput 1992。4(3):448–72.[17] Bretthorst GL. Bayesian model selection: examples relevant to NMR. In: Skilling P, editor. Maximum entropy and Bayesian methods. Dordrecht, The Netherlands: Kluwer Academic Publishers。 1990. p. 377–88.[18] Nabney IT. NETLAB: Algorithms for pattern recognition. London: Springer。 2002.本文譯自：Philippe Lauret, Eric Fock, Rija N. Randrianarivony,JeanFranc，ManiRamsamy， Bayesian neural network approach to short time load forecasting，Energy Conversion and Management 49 (2008) 1156–1166.
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