Introduction to artificial neural systems
Introduction to artificial neural systems
Modified high-order neural network for invariant pattern recognition
Pattern Recognition Letters
Center particle swarm optimization
Neurocomputing
Hybrid high order neural networks
Applied Soft Computing
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive high-order neural tree for pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using weighted genetic programming to program squat wall strengths and tune associated formulas
Engineering Applications of Artificial Intelligence
Modular neural network programming with genetic optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Improving analytical models of circular concrete columns with genetic programming polynomials
Genetic Programming and Evolvable Machines
Integrating the artificial bee colony and bees algorithm to face constrained optimization problems
Information Sciences: an International Journal
Hi-index | 12.06 |
This study explores the effectiveness of results obtained by using proposed hybrid multilayer perceptron (HMLP) networks to predict strength in concrete cylinders, reinforced-concrete deep beams, and reinforced-concrete squat walls. Such HMLP networks were designed to incorporate one linear and three high-order layer connections. Of the latter, one, employed only in the first layer connection, was derived from drawings referenced in the literature and two were developed by the author for this study. To calculate appropriate network coefficients, this study designed a center-unified particle swarm optimization (CUPSO) approach, composed of a center particle and global and local variants, which is quite effective for optimization tasks. This study gathered 103, 62, and 62 datasets, respectively, from drawings in three cases reported in the literature. Results, which showed that certain high order HMLP models perform better than their traditional counterpart, evidence the efficacy of proposed HMLP families. Each family, comprising high-order models and a linear counterpart, achieved results that were superior to those attained using traditional MLP networks only.