Model Induction Data by Dibike Y B (28 results)

Paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. pp. 160.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. pp. 160.

Condition: New. There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data. This work addresses model induction from data for the simulation of hydrodynamic processes in the aquatic environment. Num Pages: 156 pages, black & white illustrations. BIC Classification: PBWH; RBKF. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 254 x 178 x 8. Weight in Grams: 317. . 2002. Paperback. . . . . …

Paperback. Condition: New. There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data, where data refers to known samples that are combinations of inputs and corresponding outputs of a given physical system. The main subject addressed in this thesis is model induction from data for the simulation of hydrodynamic processes in the aquatic environment. Firstly, some currently popular artificial neural network architectures are introduced, and it is then argued that these devices can be regarded as domain knowledge incapsulators by applying the method to the generation of wave equations from hydraulic data and showing how the equations of numerical-hydraulic models can, in their turn, be recaptured using artificial neural networks.The book also demonstrates how artificial neural networks can be used to generate numerical operators on non-structured grids for the simulation of hydrodynamic processes in two-dimensional flow systems and a methodology has been derived for developing generic hydrodynamic models using artificial neural network. The book also highlights one other model induction technique, namely that of support vector machine, as an emerging new method with a potential to provide more robust models.…

Paperback. Condition: Brand New. 1st edition. 156 pages. 9.75x6.75x0.50 inches. In Stock.

Condition: New. There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data. This work addresses model induction from data for the simulation of hydrodynamic processes in the aquatic environment. Num Pages: 156 pages, black & white illustrations. BIC Classification: PBWH; RBKF. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 254 x 178 x 8. Weight in Grams: 317. . 2002. Paperback. . . . . Books ship from the US and Ireland. …

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New. There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data, where data refers to known samples that are combinations of inputs and corresponding outputs of a given physical system. The main subject addressed in this thesis is model induction from data for the simulation of hydrodynamic processes in the aquatic environment. Firstly, some currently popular artificial neural network architectures are introduced, and it is then argued that these devices can be regarded as domain knowledge incapsulators by applying the method to the generation of wave equations from hydraulic data and showing how the equations of numerical-hydraulic models can, in their turn, be recaptured using artificial neural networks.The book also demonstrates how artificial neural networks can be used to generate numerical operators on non-structured grids for the simulation of hydrodynamic processes in two-dimensional flow systems and a methodology has been derived for developing generic hydrodynamic models using artificial neural network. The book also highlights one other model induction technique, namely that of support vector machine, as an emerging new method with a potential to provide more robust models.…

Condition: New.

Condition: New.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New. pp. 160 66:B&W 7 x 10 in or 254 x 178 mm Perfect Bound on White w/Gloss Lam This item is printed on demand.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. YONAS BERHAN DIBIKE born in Addis Ababa, Ethiopia Master of Science with Distinction, IHE.There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data, where data refers to known samples that are combin.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data, where data refers to known samples that are combinations of inputs and corresponding outputs of a given physical system. The main subject addressed in this thesis is model induction from data for the simulation of hydrodynamic processes in the aquatic environment. Firstly, some currently popular artificial neural network architectures are introduced, and it is then argued that these devices can be regarded as domain knowledge incapsulators by applying the method to the generation of wave equations from hydraulic data and showing how the equations of numerical-hydraulic models can, in their turn, be recaptured using artificial neural networks.The book also demonstrates how artificial neural networks can be used to generate numerical operators on non-structured grids for the simulation of hydrodynamic processes in two-dimensional flow systems and a methodology has been derived for developing generic hydrodynamic models using artificial neural network. The book also highlights one other model induction technique, namely that of support vector machine, as an emerging new method with a potential to provide more robust models.…

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. YONAS BERHAN DIBIKE born in Addis Ababa, Ethiopia Master of Science with Distinction, IHE.There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data, where data refers to known samples that are com.…