The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
A particle swarm optimization algorithm for the multiple-level warehouse layout design problem
Computers and Industrial Engineering
Batch-to-batch control of fed-batch processes using control-affine feedforward neural network
Neural Computing and Applications - Special Issue: Neural networks for control, robotics and diagnostics
Classification of Single-Trial EEG Based on Support Vector Clustering during Finger Movement
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A hybrid genetic - Particle Swarm Optimization Algorithm for the vehicle routing problem
Expert Systems with Applications: An International Journal
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive pruning algorithm for least squares support vector machine classifier
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Computers and Industrial Engineering
Parameter estimation of bilinear systems based on an adaptive particle swarm optimization
Engineering Applications of Artificial Intelligence
Unit commitment problem using enhanced particle swarm optimization algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
A probabilistic SVM based decision system for pain diagnosis
Expert Systems with Applications: An International Journal
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Melt index (MI) is considered as one of the most important variables of the quality, which determines the product specifications. Thus, a reliable estimation of MI is crucial in the quality control of the practical processes in the propylene polymerization (PP). An optimal soft sensor, named the least squares support vector machines with Ant Colony-Immune Clone Particle Swarm Optimization (AC-ICPSO-LSSVM), is therefore proposed. It combines the advantages of the high accuracy of LSSVM and the fast convergence of PSO. Furthermore, the immune clone (IC) method is introduced into the PSO algorithm to make the particles of ICPSO diverse and enhance global search capability for avoiding the premature convergence and local optimization of the conventional PSO algorithm. Besides, to widen data range, improve search precision and convergence efficiency, and avoid premature convergence, Ant Colony Optimization (ACO) is introduced to find the initial particles for PSO algorithm. The resultant hybrid AC-ICPSO algorithm is then applied to optimize the parameters of LSSVM, so the optimal prediction model of melt index, AC-ICPSO-LSSVM, is obtained. As the comparative basis, the models of ICPSO-LSSVM, PSO-LSSVM, and LSSVM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. These models are also compared with RBF method reported in the open literature. The research results reveal the prediction accuracy and validity of the proposed approach.