Details of Grant 

EPSRC Reference:	GR/S00514/01
Title:	Nonlinear models design and construction algorthms (nomdec)
Principal Investigator:	Hong, Professor X
Other Investigators:
Researcher Co-Investigators:
Project Partners:	University of Southampton
Department:	Cybernetics
Organisation:	University of Reading
Scheme:	First Grant Scheme Pre-FEC
Starts:	01 January 2003	Ends:	31 December 2005	Value (£):	75,246
EPSRC Research Topic Classifications:	Control Engineering Robotics & Autonomy
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
SummaryDescribe the proposed research in about 200 words.Complex industrial and commercial processes have the potential to produce vast amounts of observational data. The automatic and intelligent construction of a system's architecture, internal dynamics and interrelationships, from measured data, is highly desirable. This would aid the autonc and robustness of plant functional performance including aspects of control, information processing, modelling, fault diagnosis, performance enhancement and high level monitoring and supervision.This research proposal points out further directions and tasks in the development of new general nonlinear design and construction algorithms th will have significant opportunities for practioners in industry and commerce. The new approach has inherent parsimony, learning convergence and n reduction properties, with sound mathematical background from the field of experimental design theory. Classes of new algorithms are developed combining conventional nonlinear modelling approaches and experimental design criteria that achieves model robustness and adequacy. These nev algorithms have clear practical use in nonlinear state estimation and controller design, data fusion, conditioning monitoring, signal processing and communication which on competition will be used by the proposers on real world bench marks including a recently awarded EU contract on marine estuarine water quality data fusion .
Key Findings
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Potential use in non-academic contexts
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Impacts
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Organisation Website:	http://www.rdg.ac.uk