Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm

Texto completo
Autor(es):
Khan, Atlas [1, 2] ; Xue, Li Zheng [1] ; Wei, Wu [1] ; Qu, YanPeng [3] ; Hussain, Amir [4] ; Vencio, Ricardo Z. N. [2]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Dalian Univ Technol, Dept Appl Math, Dalian - Peoples R China
[2] Univ Sao Paulo, Dept Comp & Math FFCLRP, Sao Paulo - Brazil
[3] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian - Peoples R China
[4] Univ Stirling, Sch Nat Sci, Div Comp Sci & Maths, Stirling FK9 4LA - Scotland
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: COGNITIVE COMPUTATION; v. 7, n. 4, p. 477-486, AUG 2015.
Citações Web of Science: 2
Resumo

The self-organizing map (SOM) approach has been used to perform cognitive and biologically inspired computing in a growing range of cross-disciplinary fields. Recently, the SOM based neural network framework was adapted to solve continuous derivative-free optimization problems through the development of a novel algorithm, termed SOM-based optimization (SOMO). However, formal convergence questions remained unanswered which we now aim to address in this paper. Specifically, convergence proofs are developed for the SOMO algorithm using a specific distance measure. Numerical simulation examples are provided using two benchmark test functions to support our theoretical findings, which illustrate that the distance between neurons decreases at each iteration and finally converges to zero. We also prove that the function value of the winner in the network decreases after each iteration. The convergence performance of SOMO has been benchmarked against the conventional particle swarm optimization algorithm, with preliminary results showing that SOMO can provide a more accurate solution for the case of large population sizes. (AU)

Processo FAPESP: 12/23329-5 - Prediction of RNA-protein binding interactions in H. salinarum using machine learning techniques
Beneficiário:Atlas Khan
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado