ARTÍCULO
TITULO

Objective Functions for Training New Hidden Units in Constructive Neural Networks

Kwok    
T-Y    
Yeung    
D-Y    

Resumen

No disponible

 Artículos similares

       
 
Liang Zhao and Yong Bai    
Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless commu... ver más

 
Abdullahi T. Sulaiman, Habeeb Bello-Salau, Adeiza J. Onumanyi, Muhammed B. Mu?azu, Emmanuel A. Adedokun, Ahmed T. Salawudeen and Abdulfatai D. Adekale    
The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards ben... ver más
Revista: Algorithms

 
Adekunle Rotimi Adekoya and Mardé Helbig    
Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, whe... ver más
Revista: Algorithms

 
Warren Hare and Gabriel Jarry-Bolduc    
This paper examines a calculus-based approach to building model functions in a derivative-free algorithm. This calculus-based approach can be used when the objective function considered is defined via more than one blackbox. Two versions of a derivative-... ver más
Revista: Algorithms

 
Manlio Gaudioso, Sona Taheri, Adil M. Bagirov and Napsu Karmitsa    
The Bundle Enrichment Method (BEM-DC) is introduced for solving nonsmooth difference of convex (DC) programming problems. The novelty of the method consists of the dynamic management of the bundle. More specifically, a DC model, being the difference of t... ver más
Revista: Algorithms