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RADIAL BASIS FUNCTION NEURAL NETWORKS WITH SEQUENTIAL LEARNING, PROGRESS IN NEURAL PROCESSING (Progress in Neural Processing, 11)
Published by World Scientific Pub Co Inc, 1715
hardcover. Condition: Very Good. Fast Shipping - Safe and Secure 7 days a week!
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Radial Basis Function Networks 2: New Advances in Design. Studies in Fuzziness and Soft Computing: Volume 67
Seller: Peter L. Masi - books, MONTAGUE, MA, U.S.A.
Association Member: SNEAB
Seller rating 4 out of 5 stars
Hardcover. Condition: Used - Very Good. Physica-Verlag, Heidelberg, copyright 2001. xix, 360 pages. Illustrated. 9.5 x 6.5", hardcover, no dj. No dj, clean, tight, very good/none.
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Condition: New. pp. 340.
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Radial Basis Function Networks 2: New Advances in Design
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 63.98
Convert currencyUS$ 16.31 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
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Radial Basis Function Neural Networks With Sequential Learning, Progress In Neural Processing
Published by World Scientific Publishing Co Pte Ltd, 1999
ISBN 10: 9810237715 ISBN 13: 9789810237714
Language: English
US$ 38.98
Convert currencyUS$ 52.25 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketCondition: Sehr gut. Zustand: Sehr gut | Seiten: 232 | Sprache: Englisch | Produktart: Bücher.
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Hardcover. Condition: New. 1st Edition.
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US$ 178.13
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US$ 178.51
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Data mining for performance of vegetative filter strips : A comparison between prediction models : artificial neural networks (back propagation & radial basis function) vs. GRAPH
Published by LAP LAMBERT Academic Publishing, 2013
ISBN 10: 365950615X ISBN 13: 9783659506154
Language: English
US$ 91.98
Convert currencyUS$ 34.48 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The vegetative filter strips (VFS) are a best management practice. For quantifying the movement & amount of sediments & nutrients, the performance of VFS has to be modeled. Data available from the literature & recent experiments were used. Artificial runoff was created. Flow samples were analysed for concentrations for total suspended solids, total phosphorus & soluble phosphorus, & particle size distribution. Input-output data sets were used to train & test a multi-layered perceptron using back propagation (BP) algorithm & a radial basis function neural network using fuzzy c-means clustering algorithm. Sensitivity tests were done for finding optimum architectures of neural networks. The statistical analysis & comparisons between predicted & observed values for the three models showed that a BP network with 15 hidden units can model the performance of VFS efficiently, including the trapping of soluble P. They could predict the outputs, even without the particle size distribution. ANN'S have to be trained before being used to predict the outputs. GRAPH is mobile & could be successfully used for verification, since it takes into account the physical processes going on.
