Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Studying possibility in a clustering algorithm for RBFNN design for function approximation
Neural Computing and Applications
Fast learning in networks of locally-tuned processing units
Neural Computation
Multiobjective RBFNNs designer for function approximation: an application for mineral reduction
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Effective input variable selection for function approximation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Analysis of input-output clustering for determining centers of RBFN
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The Nickel reduction process is a complex task where many dynamic optimization problems arises that, nowadays, requires a human operator to take decisions based on his experience and intuition. In order to help the operator to optimize the reduction process in terms of maximum amount of mineral extracted and minimum energy consumption, a control system integrated by several modules is being designed. One of the modules has the task of predicting how much petroleum will be burned in the ovens where the raw material is processed. This paper proposes an algorithm to design Radial Basis Function Neural Networks that will be able to predict accurately the amount of petroleum given a set of input parameters. The algorithm is also able of identifying the most relevant input parameters for the network so the dimensionality reduction problem is ameliorated. Hence, this paper, as it will be shown in the experiments section is able to apply the synergy of different Soft Computing techniques to the industrial process obtaining satisfactory results.