本文關(guān)鍵詞： Fisher方程 參數(shù)識(shí)別 采樣算法　出處：《黑龍江大學(xué)自然科學(xué)學(xué)報(bào)》2017年01期 　論文類型：期刊論文

【摘要】：馬爾科夫鏈蒙特卡洛方法(MCMC)是一種啟發(fā)式的全局尋優(yōu)算法。在貝葉斯框架下,給出利用MCMC方法求解Fisher方程參數(shù)識(shí)別反問(wèn)題的一種新方法。該方法把參數(shù)識(shí)別反問(wèn)題視為貝葉斯估計(jì)問(wèn)題,利用基于自適應(yīng)Metropolis算法和延遲拒絕算法的一種有效的自適應(yīng)MCMC方法,得到大量來(lái)自后驗(yàn)概率的樣本,不僅能夠獲得每個(gè)未知參數(shù)的估計(jì)值,還可以獲得與之相關(guān)的各種不確定信息。數(shù)值試驗(yàn)結(jié)果表明,該方法具有精度高、收斂速度快且易于計(jì)算機(jī)實(shí)現(xiàn)等優(yōu)點(diǎn)。
[Abstract]:Markov chain Monte Carlo method (MCMC) is a heuristic global optimization algorithm. A new method for solving the inverse problem of parameter identification of Fisher equation using MCMC method is presented. The inverse problem of parameter identification is regarded as Bayesian estimation problem, and an effective adaptive MCMC method based on adaptive Metropolis algorithm and delay rejection algorithm is used. A large number of samples from a posteriori probability can be obtained not only from the estimated values of each unknown parameter, but also from a variety of uncertain information related to it. The numerical results show that the method has a high accuracy. The convergent speed is fast and easy to be realized by computer.
【作者單位】： 大連海事大學(xué)數(shù)學(xué)系;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(41304092)
【分類號(hào)】：O241.8
，

本文編號(hào)：1537883