A Hybrid Training Algorithm for Recurrent Neural Network Using Particle Swarm Optimization-Based Preprocessing and Temporal Error Aggregation

2017 ◽  
Author(s):  
Qiao Kang ◽  
Wei-Keng Liao ◽  
Ankit Agrawal ◽  
Alok Choudhary
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Recurrent Neural Network ◽  
Particle Swarm ◽  
Training Algorithm ◽  
Swarm Optimization

scholarly journals Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Alma Y. Alanis ◽  
Luis J. Ricalde ◽  
Chiara Simetti ◽  
Francesca Odone
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Smart Grids ◽  
Network Architecture ◽  
Particle Swarm ◽  
Design Parameters ◽  
Complex Nature ◽  
Training Algorithm ◽  
Swarm Optimization

This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.

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