In this article, we focus on discussing the degree distribution of the DMS model from the perspective of probability. On the basis of the concept and technique of first-passage probability in Markov theory, we provide...In this article, we focus on discussing the degree distribution of the DMS model from the perspective of probability. On the basis of the concept and technique of first-passage probability in Markov theory, we provide a rigorous proof for existence of the steady-state degree distribution, mathematically re-deriving the exact formula of the distribution. The approach based on Markov chain theory is universal and performs well in a large class of growing networks.展开更多
From the perspective of probability, the stability of a modified Cooper- Frieze model is studied in the present paper. Based on the concept and technique of the first-passage probability in the Markov theory, we provi...From the perspective of probability, the stability of a modified Cooper- Frieze model is studied in the present paper. Based on the concept and technique of the first-passage probability in the Markov theory, we provide a rigorous proof for the exis- tence of the steady-state degree distribution, and derive the explicit formula analytically. Moreover, we perform extensive numerical simulations of the model, including the degree distribution and the clustering.展开更多
在可生长结构网络(Growing When Required Network,简称GWRN)的基础上,提出了有监督可生长结构网络(Supervised Growing When Required Network,简称SGWRN)模型。该模型引入线性输出层,将GWRN与径向基函数结合,构成有监督自组织神经网...在可生长结构网络(Growing When Required Network,简称GWRN)的基础上,提出了有监督可生长结构网络(Supervised Growing When Required Network,简称SGWRN)模型。该模型引入线性输出层,将GWRN与径向基函数结合,构成有监督自组织神经网络学习模型。该模型能快速生长,可广泛用于监督学习。倒立摆平衡控制仿真实验结果表明该模型有效。展开更多
基金supported by the National Natural Science Foundation (11071258, 60874083, 10872119, 10901164)
文摘In this article, we focus on discussing the degree distribution of the DMS model from the perspective of probability. On the basis of the concept and technique of first-passage probability in Markov theory, we provide a rigorous proof for existence of the steady-state degree distribution, mathematically re-deriving the exact formula of the distribution. The approach based on Markov chain theory is universal and performs well in a large class of growing networks.
基金supported by the National Natural Science Foundation of China (No. 10671212)
文摘From the perspective of probability, the stability of a modified Cooper- Frieze model is studied in the present paper. Based on the concept and technique of the first-passage probability in the Markov theory, we provide a rigorous proof for the exis- tence of the steady-state degree distribution, and derive the explicit formula analytically. Moreover, we perform extensive numerical simulations of the model, including the degree distribution and the clustering.
文摘在可生长结构网络(Growing When Required Network,简称GWRN)的基础上,提出了有监督可生长结构网络(Supervised Growing When Required Network,简称SGWRN)模型。该模型引入线性输出层,将GWRN与径向基函数结合,构成有监督自组织神经网络学习模型。该模型能快速生长,可广泛用于监督学习。倒立摆平衡控制仿真实验结果表明该模型有效。
