New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process

By Ying Song, Zengqiang Chen, Zhuzhi Yuan. 2007

188 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach (2015, methodology): “The use of ANN to predict the behavior and tendencies of stocks has demonstrated itself to be a viable alternative to existing conventional techniques (Andrade de Oliveira and Nobre, 2011; Naeini et al., 2010; Song et al., 2007; Lee and Chen, 2007; Ma et al., 2010).”
Self regulating particle swarm optimization algorithm (2015, background): “In recent years, many real-world optimization problems such as nonlinear process control [63], structural design [69], grouping problems [26], text mining [8], etc.”
Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms at Training of Neural Network for prediction (2014, methodology): “Neural networks are used in combination with PSO in many applications, like neural network control for nonlinear processes in [10], feedforward neural network training in [11] – [17].”
A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, methodology): “Now, the PSO has been extended to power systems [7]–[9], artificial neural network training [10], [11], fuzzy system control [12], [13], image processing [14], [15], and so on.”
Comparative study between Differential Evolution and Particle Swarm Optimization algorithms in training of feed-forward neural network for stock price prediction (2010, methodology): “A differential evolution based neural network training algorithm, introduced in [5, 10 , 23].”