An information theoretic approach for combining neural network process models

Sridhar, Dasaratha V.; Barlett, E. B.; Seagrave, Richard C.

doi:10.1016/s0893-6080(99)00030-1

Cited by 61 publications

(31 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SNNs are commonly used in the literature for the ensemble combination of NNs organized in two levels [13]. We extend the concept of SNNs to a modular combination of NNs for an unlimited hierarchical number of levels.…”

Section: Multi-sensor Data Merging With Stacked Nnsmentioning

confidence: 99%

Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

Loyola

Coldewey‐Egbers

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to learn the temporal and spatial drifts between data from different satellite sensors. The resulting NNs are then used to sequentially adjust the satellite data for the creation of a global homogeneous long-term climate data record. The proposed technique has successfully been applied to the merging of ozone data from three European satellite sensors covering together a time period of more than 16 years. The resulting long-term ozone data record has an excellent long-term stability of 0.2 ± 0.2% per decade and can therefore be used for ozone and climate studies.

show abstract

Section: Multi-sensor Data Merging With Stacked Nnsmentioning

confidence: 99%

Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

Loyola

Coldewey‐Egbers

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Optimal subset selection using information theory is widely used in other fields such as the pattern recognition and neural networks fields. Sridhar, Bartlett and Seagrave (1999) proposed an algorithm using information theory for combining neural network models. This algorithm identifies and combines useful models regardless of the nature of their relationship to the actual output.…”

Section: Information Theorymentioning

confidence: 99%

A combination selection algorithm on forecasting

Cang

2014

European Journal of Operational Research

View full text Add to dashboard Cite

It is widely accepted in forecasting that a combination model can improve forecasting accuracy. One important challenge is how to select the optimal subset of individual models from all available models without having to try all possible combinations of these models. This paper proposes an optimal subset selection algorithm from all individual models using information theory. The experimental results in tourism demand forecasting demonstrate that the combination of the individual models from the selected optimal subset significantly outperforms the combination of all available individual models. The proposed optimal subset selection algorithm provides a theoretical approach rather than experimental assessments which dominate literature.

show abstract

“…Another contribution by Sridhar et al . [32] in proving the superiority of multiple neural networks was by using the stacked neural networks together with information theoretic stacking (ITS) algorithm. This algorithm was used to combine neural network models.…”

Section: Modeling For Chemical Processes Using Stacked Neural Networkmentioning

confidence: 99%

Multiple neural networks modeling techniques in process control: a review

Ahmad

Noor

Zhang

2009

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

This paper reviews new techniques to improve neural network model robustness for nonlinear process modeling and control. The focus is on multiple neural networks. Single neural networks have been dominating the neural network 'world'. Despite many advantages that have been mentioned in the literature, some problems that can deteriorate neural network performance such as lack of generalization have been bothering researchers. Driven by this, neural network 'world' evolves and converges toward better representations of the modeled functions that can lead to better generalization and manages to sweep away all the glitches that have shadowed neural network applications. This evolution has lead to a new approach in applying neural networks that is called as multiple neural networks. Just recently, multiple neural networks have been broadly used in numerous applications since their performance is literally better than that of those using single neural networks in representing nonlinear systems. 

show abstract

An information theoretic approach for combining neural network process models

Cited by 61 publications

References 17 publications

Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

A combination selection algorithm on forecasting

Multiple neural networks modeling techniques in process control: a review

Contact Info

Product

Resources

About