| Full text | |
| Author(s): |
Khan, Atlas
[1, 2]
;
Xue, Li Zheng
[1]
;
Wei, Wu
[1]
;
Qu, YanPeng
[3]
;
Hussain, Amir
[4]
;
Vencio, Ricardo Z. N.
[2]
Total Authors: 6
|
| Affiliation: | [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
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | COGNITIVE COMPUTATION; v. 7, n. 4, p. 477-486, AUG 2015. |
| Web of Science Citations: | 2 |
| Abstract | |
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) | |
| FAPESP's process: | 12/23329-5 - Prediction of RNA-protein binding interactions in H. salinarum using machine learning techniques |
| Grantee: | Atlas Khan |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |