The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally...The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally, through a competitive learning process, the SOMO algorithm searches for the minimum of an objective function. The MaxMin-SOMO algorithm is the generalization of SOMO with two winners for simultaneously finding two winning neurons i.e., first winner stands for minimum and second one for maximum of the objective function. In this paper, the convergence analysis of the MaxMin-SOMO is presented. More specifically, we prove that the distance between neurons decreases at each iteration and finally converge to zero. The work is verified with the experimental results.展开更多
Stable porphyrinoid-based radicals and diradicaloids that possess delocalizedπ-systems can be highly advantageous for use in spintronic and electronic applications.In this work,we present the facile synthesis of a ne...Stable porphyrinoid-based radicals and diradicaloids that possess delocalizedπ-systems can be highly advantageous for use in spintronic and electronic applications.In this work,we present the facile synthesis of a neutral 17π-electron nickel corrole monoradical with a SOMO-HOMO degenerate electronic configuration.展开更多
基金supported by National Natural Science Foundation of China(Nos.11171367 and 61502068)the Fundamental Research Funds for the Central Universities of China(No.3132014094)+1 种基金the China Postdoctoral Science Foundation(Nos.2013M541213 and 2015T80239)Fundacao da Amaro a Pesquisa do Estado de Sao Paulo(FAPESP)Brazil(No.2012/23329-5)
文摘The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally, through a competitive learning process, the SOMO algorithm searches for the minimum of an objective function. The MaxMin-SOMO algorithm is the generalization of SOMO with two winners for simultaneously finding two winning neurons i.e., first winner stands for minimum and second one for maximum of the objective function. In this paper, the convergence analysis of the MaxMin-SOMO is presented. More specifically, we prove that the distance between neurons decreases at each iteration and finally converge to zero. The work is verified with the experimental results.
基金supported by the National Natural Science Foundation of China(Grants.22271140,22071103,22001119 and 21911540069)the Program A for Outstanding PhD Candidate of Nanjing University(Grant.202201A006).
文摘Stable porphyrinoid-based radicals and diradicaloids that possess delocalizedπ-systems can be highly advantageous for use in spintronic and electronic applications.In this work,we present the facile synthesis of a neutral 17π-electron nickel corrole monoradical with a SOMO-HOMO degenerate electronic configuration.
