Universal approximation using radial-basis-function networks
Neural Computation
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
Advances in Engineering Software
Expert Systems with Applications: An International Journal
Multiple low voltage power flow solutions using hybrid PSO and optimal multiplier method
Expert Systems with Applications: An International Journal
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Melt index (MI) is considered as an important quality variable which determines the product specifications, so reliable estimation of MI is crucial in the quality control of practical polypropylene (PP) polymerization processes. A novel MPSO-SA-RNN (modified PSO-SA algorithm and RBF neural network) MI prediction model based on radial basis function (RBF) neural network and artificial intelligent algorithms particle swarm optimization (PSO), and simulated annealing (SA) is presented, where the traditional PSO is modified first and then combined with SA to overcome the inherent defects in PSO and SA, and to achieve better optimization performance. The proposed optimization algorithm, MPSO-SA algorithm, is then used to optimize the parameters of the RBF neural network. Then the network is employed to build the MI prediction model, and the MPSO-SA-RNN model is thereby developed. Based on the data from a real plant, the approach presented above is evaluated and the research results confirm the validity of the proposed model, as well as the advantage of MPSO-SA algorithm to the traditional PSO and SA algorithms in handling optimization problems.