Paper Info
Title
Optimization of feedforward neural network by Multiple Particle Collision Algorithm
Abstract
Optimization of neural network topology, weights and neuron activation functions for given data set and problem is not an easy task. In this article, a technique for automatic configuration of parameters topology for feedforward artificial neural networks (ANN) is presented. The determination of optimal parameters is formulated as an optimization problem, solved with the use of meta-heuristic Multiple Particle Collision Algorithm (MPCA). The self-configuring networks are applied to predict the mesoscale climate for the precipitation field. The results obtained from the neural network using the method of data reduction by the Theory of Rough Sets and the self-configuring network by MPCA were compared.
Year
DOI
Venue
2014
10.1109/FOCI.2014.7007817
Foundations of Computational Intelligence
Keywords
Field
DocType
data reduction,feedforward neural nets,optimisation,rough set theory,ANN,MPCA,data reduction,feedforward neural network optimization,metaheuristic multiple particle collision algorithm,parameter topology automatic configuration,rough set theory,self-configuring network,self-configuring networks
Feedforward neural network,Computer science,Stochastic neural network,Algorithm,Recurrent neural network,Probabilistic neural network,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Deep learning,Artificial neural network,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Juliana A. Anochi100.34
Haroldo F. De Campos Velho2125.42