By I. M. Mujtaba, Mehboob A. Hussain
This quantity is a follow-up to the IChemE symposium on "Neural Networks and different studying Technologies", held at Imperial collage, London, in could 1999. The curiosity proven by way of the individuals, particularly these from the undefined, has been instrumental in generating the e-book. The papers were written by means of members to the symposium and specialists within the box from worldwide. They current all of the very important points of neural community usage in addition to express the flexibility of neural networks in a number of elements of technique engineering difficulties - modelling, estimation, keep watch over, optimization and commercial functions.
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Additional info for Application of Neural Networks and Other Learning Technologies in Process Engineering
Model orders of mj = 0 or nk = 0 indicate that the corresponding y'-th output or k-th input is not relevant to the process output being modeled. A scalar network output is used so that one MISO RBFN model has to be RBFN Identification of an Industrial Polymerization Reactor Model 25 identified for each output of the process, and each MISO model may have a different input vector. The dead time for each input is 9k, and may also be different for each input and each model. For RBFN modeling, the input and output data are usually normalized between zero and one.
D. BOMBERGER E. I. A. D. E. A. B. A. OGUNNAIKE E. I. A. Methods developed for radial basis function network (RBFN) identification are applied to a complex multiple-input, multiple-output (MIMO) simulation of a solution copolymerization reactor. For RBFN identification, k-means clustering and stepwise regression analysis are used. The practicality of applying these methods to large industrial identification problems is discussed, considering the restrictions of industrially practical input sequence design.
A. OGUNNAIKE E. I. A. Methods developed for radial basis function network (RBFN) identification are applied to a complex multiple-input, multiple-output (MIMO) simulation of a solution copolymerization reactor. For RBFN identification, k-means clustering and stepwise regression analysis are used. The practicality of applying these methods to large industrial identification problems is discussed, considering the restrictions of industrially practical input sequence design. The RBFN model has three inputs and two outputs, and the dimensionality of the identification problem poses some difficulties for nonlinear empirical model identification; specifically, the large amount of data required is a problem for plant testing and may cause computational difficulties for identification algorithms as well.
Application of Neural Networks and Other Learning Technologies in Process Engineering by I. M. Mujtaba, Mehboob A. Hussain