Constructive Neural Network In Model-based Control Of A Biotechnological Process

dc.creatorMeleiro L.A.C.
dc.creatorMaciel Filho R.
dc.creatorVon Zuben F.J.
dc.date2003
dc.date2015-06-30T17:32:41Z
dc.date2015-11-26T14:12:20Z
dc.date2015-06-30T17:32:41Z
dc.date2015-11-26T14:12:20Z
dc.date.accessioned2018-03-28T21:12:57Z
dc.date.available2018-03-28T21:12:57Z
dc.identifier
dc.identifierProceedings Of The International Joint Conference On Neural Networks. , v. 3, n. , p. 2406 - 2411, 2003.
dc.identifier
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-0141837224&partnerID=40&md5=7b1a689793103134658070598d2bf981
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/102573
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/102573
dc.identifier2-s2.0-0141837224
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1241967
dc.descriptionIn the present work, a constructive learning algorithm is employed to design an optimal one-hidden layer neural network structure that best approximates a given mapping. The method determines not only the optimal number of hidden neurons but also the best activation function for each node. Here, the projection pursuit technique is applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is optimally defined for every approximation problem, better rates of convergence are achieved. The proposed constructive learning algorithm was successfully applied to identify a large-scale multivariate process, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model is then considered as part of a model-based predictive control strategy, with high-quality performance in closed-loop experiments.
dc.description3
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dc.description2406
dc.description2411
dc.descriptionAndrietta, S.R., Maugeri, F., Optimum design of a continuous fermentation unit of an industrial plant for alcohol production (1994) Advances in Bioprocess Engineering, , Kluwer Academic Publishers
dc.descriptionBärmann, F., Biegler-König, F., On a Class of Efficient Learning Algorithms for Neural Networks (1992) Neural Network, 5 (1), pp. 139-144
dc.descriptionBattiti, R., First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method (1992) Neural Computation, 4 (2), pp. 141-166
dc.descriptionCamacho, E.F., Bordons, C., (1999) Model Predictive Control, , Springer-Verlag"
dc.descriptionClarke, D.W., (1994) Advances in Model Based Predictive Control, , Oxford University Press
dc.descriptionEdgar, T.F., Himmelblau, D.M., (1988) Optimization of Chemical Processes, , McGraw-Hill
dc.descriptionFriedman, J.H., Stuetzle, W., Projection Pursuit Regression (1981) Journal of the American Statistical Association (JASA), 76, pp. 817-823
dc.descriptionGeman, S., Bienenstock, E., Doursat, R., Neural Networks and the Bias/Variance Dilemma (1992) Neural Computation, 4 (1), pp. 1-58
dc.descriptionHaykin, S., (1999) Neural Networks: A Comprehensive Foundation - 2nd Edition, , Prentice Hall
dc.descriptionHornik, K., Multilayer feedforward networks are universal approximators (1989) Neural Networks, 2 (5), pp. 359-366
dc.descriptionHuber, P.J., Projection Pursuit (1985) The Annal a of Statistics, 13 (2), pp. 435-475
dc.descriptionHwang, J.-N., Lay, S.-R., Maechler, M., Martin, R.D., Schimert, J., Regression Modeling in Back-Propagation and Projection Pursuit Learning (1994) IEEE Transactions on Neural Networks, 5 (3), pp. 342-353
dc.descriptionJones, L.K., On a conjecture of Huber concerning the convergence of projection pursuit regression (1987) The Annals of Statistics, 15, pp. 880-882
dc.descriptionKosko, B., (1997) Fuzzy Engineering, , Prentice Hall
dc.descriptionKosko, B., (1992) Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, , Prentice Hall
dc.descriptionKwok, T.Y., Yeung, D.Y., Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems (1997) IEEE Trans. on Neural Networks, 8 (3), pp. 630-645
dc.descriptionMeleiro, L.A.C., (2002) Design and Applications of Linear, Neural, and Fuzzy Model-Based Controllers, , PhD Thesis (in Portuguese)
dc.descriptionMeleiro, L.A.C., Campello, R.J.G.B., Maciel Filho, R., Von Zuben, F.J., Identification of a Multivariate Fermentation Process Using Constructive Learning (2002) Proc. SBRN'2002 - VII Brazilian Symposium on Neural Networks, , IEEE Computer Society
dc.descriptionMeleiro, L.A.C., Maciel Filho, R., Campello, R.J.G.B., Amaral, W.C., Hierarchical Neural Fuzzy Models as a Tool for Process Identification: A Bioprocess Application (2001) Application of Neural Networks and Other Learning Technologies in Process Engineering, , Mujtaba, I. M. and Hussain, M. A. (Editors). Imperial College Press
dc.descriptionNg, G.W., (1997) Application of Neural Networks to Adaptive Control of Nonlinear Systems, , Research Studies Press Ltd., John Wiley & Sons Inc
dc.descriptionRoosen, C.B., Hastie, T.J., Automatic Smoothing Spline Projection Pursuit (1994) Journal of Computational and Graphical Statistics, 3, pp. 235-248
dc.descriptionSoeterboek, R., (1992) Predictive Control - A Unified Approach, , Prentice Hall
dc.descriptionVon Zuben, F.J., Netto, M.L.A., Unit-growing learning optimizing the solvability condition for model-free regression (1995) Proceedings of the IEEE International Conference on Neural Networks, 2, pp. 795-800
dc.descriptionVon Zuben, F.J., Netto, M.L.A., Projection Pursuit and the Solvability Condition Applied to Constructive Learning (1997) Proceedings of the International Joint Conference on Neural Networks, pp. 1062-1067. , Houston - USA, 2
dc.languageen
dc.publisher
dc.relationProceedings of the International Joint Conference on Neural Networks
dc.rightsfechado
dc.sourceScopus
dc.titleConstructive Neural Network In Model-based Control Of A Biotechnological Process
dc.typeActas de congresos


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