Area:
Electronics and Electrical Engineering
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High-probability grants
According to our matching algorithm, Irwin W. Sandberg is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
1990 — 1991 |
Sandberg, Irwin |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nonlinear Systems and Representations For Input-Output Maps @ University of Texas At Austin
The objective of this project is to conduct research concerning the qualitative properties of nonlinear systems, with emphasis on the theory of representations. The research proposed concerns in part an interesting new type of representation that is an extension of the familiar impulse-response representation for linear systems. Applications involving, for example, stability theory and systems governed by nonlinear differential equations are considered. Related nonlinear-system research concerning representations, stability, iteration and convergence, etc., is also pursued and this would bear on, for example, nonlinear filtering for image processing. The tools to be used include topology, functional analysis, and integration theory.
|
0.915 |
1994 — 1998 |
Sandberg, Irwin Ghosh, Joydeep (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Function-Space Neural Networks For Spatio-Temporal Transformation and Pattern Recognition @ University of Texas At Austin
9307632 Sandberg Nonlinear transformation and recognition of continuous-time signals or signal sequences is fundamental to a wide range of cognitive processes. This work aims to build a comprehensive understanding of the processing of spatio-temporal signals. It is founded on recent results obtained by the proposer showing that very large classes of continuous functionals and shift-invariant functional maps can be uniformly approximated by certain conceptually simple neural-like structures. These structures are the Function Space Neural Networks (FSNNs) that involve a preprocessing linear operation stage such as convolution with suitable kernel functions, followed by a network of nonlinear cells. The project shall address key issues pertaining to the design and use of FSNNs. These include determination of suitable kernel functions, network size, connectivity patterns and form of nonlinearity for different problems classes, effectiveness of alternate learning algorithms and their convergence rates, and techniques for constructive/destructive network growth. Anew call of FSNNs based on higher order networks that have proved very effective for static classification, will also propose be evaluated. Theoretical studies shall be supplemented by extensive simulations using several suites of spatio-temporal signals ranging form low-dimensional artificial patterns to a set of over 1000 short duration signals representing actual passive sonar returns form underwater biologics. This work shall try to raise the understanding of the capabilities and usability of artificial neural network structures for the processing of spatio-temporal signals to a level comparable to that achieved at present for (static) multilayered feedforward networks. ***
|
0.915 |
1997 — 2001 |
Sandberg, Irwin |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Approximation and Nonlinear Networks @ University of Texas At Austin
ECS-9703745 Sandberg The general problem of understanding nonlinear mappings from one function space into another is fundamental to a wide range of engineering system studies. The proposed work aims to build a comprehensive understanding of the approximation capabilities, usability and effectiveness of nonlinear networks intended for use in settings involving compensation, adaptivity, identification and signal processing. It is founded on recent results obtained by the proposer showing that very large classes of continuous functionals, shift-invariant maps, and shift-varying maps can be uniformly approximated by certain conceptually simple nonlinear structures. The project will address key issues concerning the advantages, limitations, design and use of such structures. These include the determination of suitable network structures, network size, connectivity patterns and form of nonlinearity for different problem classes, effectiveness of alternate identification algorithms and their convergence rates, and techniques for constructive/destructive network growth. The proposed work will emphasize the development of an analytical basis for design, and will raise the understanding of the capabilities and usability of these nonlinear structures to a level comparable to that achieved at present for (static) multilayered feedforward networks. Important examples of systems to which our results will apply can be drawn from many fields.
|
0.915 |