THE INFLUENCE OF TRAINING DATA SELECTION ON PERFORMANCE OF NEURAL NETWORKS FOR CONTROL OF NON-LINEAR SYSTEMS**Presented at 1992 IFAC/IFIP/IMACS International Symposium on Artificial Intelligence in Real-Time Control, Delft, the Netherlands, June 16–18, 1992

1993 ◽  
pp. 55-60
Author(s):  
A.B. Bendtsen ◽  
Niels Jensen
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
The Netherlands ◽  
International Symposium ◽  
Training Data ◽  
Data Selection ◽  
Real Time Control ◽  
Time Control ◽  
Non Linear Systems ◽  
Training Data Selection

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

A novel approach to the real-time modelling of large urban drainage systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 19-24 ◽  
Author(s):  
Richard Norreys ◽  
Ian Cluckie
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Drainage System ◽  
Radar Data ◽  
Urban Drainage ◽  
Real Time Control ◽  
Empirical Modelling ◽  
Time Control ◽  
Novel Approach ◽  
Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

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