SBIR Phase I: Nonlinear Modeling in the Presence of Disturbances Using Support Vector Machines

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$99,977.00
Award Year:
2004
Program:
SBIR
Phase:
Phase I
Contract:
0339249
Award Id:
69353
Agency Tracking Number:
0339249
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
5970 Heisley Road, Mentor, OH, 44060
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Stephen Piche
PI
(440) 357-7794
nparikh@pegasustec.com
Business Contact:
Patrick Flaherty
(440) 358-7740
pflaherty@pegasustec.com
Research Institution:
n/a
Abstract
This Small Business Innovation Research Phase I project will investigate novel algorithms for developing nonlinear models based upon time series data that is affected by disturbances. The Box-Jenkins algorithm has been the standard approach over the past few decades for developing models for time-series systems that are affected by disturbances. In recent years, Support Vector Machines (SVMs) have been used to create accurate nonlinear models based upon empirical data. The proposed SBIR research will investigate combining SVM modeling approaches with Box-Jenkins type disturbance rejection techniques. Such an approach would be significantly more computationally efficient, thus, allowing commercialization of the algorithms. Because modeling of nonlinear time-series based systems that are affected by disturbances is commonly encountered across a wide variety of fields including economics, the process industries, engineering, psychology and defense, the proposed research has wide applicability.

* information listed above is at the time of submission.

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