Identification of Type-2 Fuzzy Models for Time-Series Forecasting Using Particle Swarm Optimization

2012 ◽  
Author(s):  
Mamta Khosla ◽  
Rakesh Kumar Sarin ◽  
Moin Uddin
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Series Forecasting ◽  
Swarm Optimization ◽  
Fuzzy Models

scholarly journals Time Series Forecasting: A Hybrid Neuro-Fuzzy-Particle Swarm Optimization Model

2020 ◽  
Vol 176 (40) ◽  
pp. 1-8
Author(s):  
Muneer Abdullah Saeed Hazaa Al-Mekhlafy ◽  
Muneef Abdulkarim Farea Ahmed ◽  
Mohamed Ghaleb Yosuef
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Particle Swarm ◽  
Time Series Forecasting ◽  
Swarm Optimization ◽  
Neuro Fuzzy

scholarly journals Time Series Forecasting Using a Hybrid Adaptive Particle Swarm Optimization and Neural Network Model

2014 ◽  
Vol 2 (4) ◽  
pp. 335-344 ◽  
Author(s):  
Yi Xiao ◽  
John J. Liu ◽  
Yi Hu ◽  
Yingfeng Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Series Forecasting ◽  
Swarm Optimization ◽  
Individual Fitness ◽  
Noise Data ◽  
Multilayer Feedforward Neural Network ◽  
The Individual

AbstractFor time series forecasting, the problem that we often encounter is how to increase the prediction accuracy as much as possible with the irregular and noise data. This study proposes a novel multilayer feedforward neural network based on the improved particle swarm optimization with adaptive genetic operator (IPSO- MLFN). In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Further, a crossover rate which only depends on generation and does not associate with the individual fitness is designed. Finally, the parameters of MLFN are optimized by IPSO. The empirical results on the container throughput forecast of Shenzhen Port show that forecasts with IPSO-MLFN model are more conservative and credible.

Sign in / Sign up

Export Citation Format

Share Document