The Method for Single Well Operational Cost Prediction Combining RBF Neural Network and Improved PSO Algorithm

2018 ◽  
Author(s):  
Hongtao Hu ◽  
Jinrong Feng ◽  
Xiaojing Zhai ◽  
Xin Guan
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Cost Prediction ◽  
Single Well ◽  
Improved Pso

scholarly journals Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm

2019 ◽  
Vol 9 (13) ◽  
pp. 2589 ◽  
Author(s):  
Jian Dong ◽  
Yingjuan Li ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Surrogate Model ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Search Space ◽  
Population Diversity ◽  
Antenna Design ◽  
Multi Objective ◽  
Antenna Performance ◽  
Improved Pso

In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment and update mechanism simultaneously, two modifications are proposed in this improved PSO. First, time-varying learning factors are designed to balance exploration and exploitation ability of particles in the search space. Second, the local best information is added to the updating process of particles except for personal and global best information for better population diversity. The improved PSO is applied to train RBFNN for determining optimal network parameters. As a result, the constructed improved PSO-RBFNN model can be used as a surrogate model for antenna performance prediction with better network generalization capability. By integrating the improved PSO-RBFNN surrogate model with multi-objective evolutionary algorithms (MOEAs), a fast multi-objective antenna optimization framework for multi-parameter antenna structures is then established. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, demonstrating that the proposed model provides better prediction performance and considerable computational savings compared to those previously published approaches.

A Rough-Fuzzy RBF Neural Network Based on PSO Algorithm

2013 ◽  
Vol 710 ◽  
pp. 617-622
Author(s):  
Jing Zhao
Keyword(s):  
Neural Network ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Training Sample ◽  
Network Training ◽  
Acquisition Method

A Rough-Fuzzy RBF Neural Network was raised based on PSO Algorithm. In this model,gives a knowledge acquisition method that based on rough set theory,the Rough-Fuzzy RBF neural network are constructed according to the results of the knowledge acquisition,the PSO are used to optimize the network parameters.This paper take number plate for example to conduct a simulation experiment.The results shows that the model can simplify the network training sample,optimize the network structure and enhance the systems study efficiency and the precision.

Algorithm Researching of RBF Neural Network Based on Improved PSO

2011 ◽  
Vol 179-180 ◽  
pp. 233-238 ◽  
Author(s):  
Hua Chen ◽  
Yi Ren Fan ◽  
Shao Gui Deng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Parameter Vector ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Whole Space ◽  
Best Parameter ◽  
Improved Pso

In view of the defect of particle swarm optimization which easily gets into partial extremum, the paper put out an improved particle swarm optimization, and applies the algorithm to the selecting of parameter of RBF neural network basal function. It searches the best parameter vector in the whole space, according to coding means, iterative formula, adapted function which the paper puts forwards. The experiment proves that RBF neural network based on improved PSO has faster convergent speed, and higher error precision.

Sign in / Sign up

Export Citation Format

Share Document