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A neural network algorithm for syste...

Velas, John P.

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A neural network algorithm for system modeling, global extrapolation, and parameter estimation for acoustical data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A neural network algorithm for system modeling, global extrapolation, and parameter estimation for acoustical data./
作者:	Velas, John P.
面頁冊數:	238 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-12, Section: B, page: 6176.
Contained By:	Dissertation Abstracts International64-12B.
標題:	Engineering, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3114902

A neural network algorithm for system modeling, global extrapolation, and parameter estimation for acoustical data.
Velas, John P.

A neural network algorithm for system modeling, global extrapolation, and parameter estimation for acoustical data. - 238 p.

Source: Dissertation Abstracts International, Volume: 64-12, Section: B, page: 6176.

Thesis (Ph.D.)--The Pennsylvania State University, 2003.

This thesis presents a coherent set of three neural-network based techniques to solve the related problems of system modeling, global function extrapolation, and parameter estimation for a class of problems that can be described as being of strongly separated type. The coherency derives from a semigroup property which is first developed in the model, is then continued in the extrapolation process, and is finally used as a basis for parameter estimation. It is intended primarily for application to the fields of acoustics and nonlinear vibrations, but can be generalized to other areas such as fluid flow and heat flow. It is assumed throughout that no prior analytical description of the data exists.Subjects--Topical Terms:

1020744
Engineering, General.

A neural network algorithm for system modeling, global extrapolation, and parameter estimation for acoustical data.
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245 1 2 $a A neural network algorithm for system modeling, global extrapolation, and parameter estimation for acoustical data.
300 $a 238 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-12, Section: B, page: 6176.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2003.
520 $a This thesis presents a coherent set of three neural-network based techniques to solve the related problems of system modeling, global function extrapolation, and parameter estimation for a class of problems that can be described as being of strongly separated type. The coherency derives from a semigroup property which is first developed in the model, is then continued in the extrapolation process, and is finally used as a basis for parameter estimation. It is intended primarily for application to the fields of acoustics and nonlinear vibrations, but can be generalized to other areas such as fluid flow and heat flow. It is assumed throughout that no prior analytical description of the data exists.
520 $a The system modeling technique requires that the resulting approximating function lends itself to a particular interpretation involving the product of a coefficient vector which is dependent on one system variable with a basis set of vectors, which are dependent on the remaining system variables, where the coefficient vector possesses a semigroup property. Extrapolation of the model reduces to extrapolation of the coefficient vector and consists of continuing the semigroup property which produced the original coefficient vector. Parameter estimation is based on establishing a linear relationship between a given coefficient vector with an unknown system parameter and a reference coefficient vector with a known system parameter. Under certain circumstances, the elements of the matrix which measure the relationship between coefficient vectors can be put into 1-1 correspondence with the system parameters.
520 $a The neural network implementation has several novel features. Concerning the architecture, semigroup theory requires that the neural network realization of the system modeling consists of dual channels, one of which is implementing a finite-dimensional function space and the other of which is selecting a specific function from within the function space. Concerning the operation, semigroup theory requires that the selection channel operates as a semigroup of operators. Concerning the extrapolation, it is based on a two-tier interpretation of training. On the lower tier, the network is trained to replicate a sequence of incrementally longer and longer sections of the overall coefficient trajectory. On the upper tier, the sequence of incremental weight changes is monitored. Under certain circumstances, the sequence of incremental weight changes converges and becomes differential in magnitude. (Abstract shortened by UMI.)
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