Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multivariable System Identification for Process Control
Multivariable System Identification for Process Control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Fuzzy Logic, Identification and Predictive Control (Advances in Industrial Control)
Fuzzy Logic, Identification and Predictive Control (Advances in Industrial Control)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Introducing robustness in multi-objective optimization
Evolutionary Computation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
Evolutionary multiobjective industrial design: the case of a racing car tire-suspension system
IEEE Transactions on Evolutionary Computation
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
IEEE Transactions on Evolutionary Computation
Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms for multivariable PI controller design
Expert Systems with Applications: An International Journal
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A dynamic predictive-control model of a nonlinear and temporal process is considered. Evolutionary computation and data mining algorithms are integrated for solving the model. Data-mining algorithms learn dynamic equations from process data. Evolutionary algorithms are then applied to solve the optimization problem guided by the knowledge extracted by data-mining algorithms. Several properties of the optimization model are shown in detail, in particular, a selection of regressors, time delays, prediction and control horizons, and weights. The concepts proposed in this paper are illustrated with an industrial case study in combustion process.