萨斯卡·博宁和马丁·格朗姆于2014年在《社会科学研究网》(SSRN)发布的文章《商誉会计:文献评述》(Goodwill Accounting:A Review of the Literature),对美国公认会计原则和国际财务报告准则引入商誉"单一减值法"后...萨斯卡·博宁和马丁·格朗姆于2014年在《社会科学研究网》(SSRN)发布的文章《商誉会计:文献评述》(Goodwill Accounting:A Review of the Literature),对美国公认会计原则和国际财务报告准则引入商誉"单一减值法"后,各国学者有关商誉会计的实证研究文献进行了综合评述。研究文献涉及商誉会计准则的发展、商誉是否满足资产定义、商誉"摊销法"和"单一减值法"的影响等方面。本文对其中较高参考价值部分进行了编译。展开更多
Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value ...Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.展开更多
In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demons...In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.展开更多
文摘萨斯卡·博宁和马丁·格朗姆于2014年在《社会科学研究网》(SSRN)发布的文章《商誉会计:文献评述》(Goodwill Accounting:A Review of the Literature),对美国公认会计原则和国际财务报告准则引入商誉"单一减值法"后,各国学者有关商誉会计的实证研究文献进行了综合评述。研究文献涉及商誉会计准则的发展、商誉是否满足资产定义、商誉"摊销法"和"单一减值法"的影响等方面。本文对其中较高参考价值部分进行了编译。
文摘Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.
基金the National Science & Technology Major Projects(Grant No.2008ZX05023-005-013).
文摘In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.
