Sensitivity analysis of prior knowledge in knowledge-based neurocomputing

2005 ◽  
Author(s):  
I. Cloete ◽  
S. Snyders ◽  
D.S. Yeung ◽  
X. Wang
Keyword(s):  
Sensitivity Analysis ◽  
Prior Knowledge ◽  
Knowledge Based

scholarly journals Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

2017 ◽  
Vol 221 ◽  
pp. 427-436 ◽  
Author(s):  
Anthony L. Schroeder ◽  
Dalma Martinović-Weigelt ◽  
Gerald T. Ankley ◽  
Kathy E. Lee ◽  
Natalia Garcia-Reyero ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
River Basin ◽  
Biological Effects ◽  
Knowledge Based

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

scholarly journals Optimal prior knowledge-based direction of arrival estimation

2012 ◽  
Vol 6 (8) ◽  
pp. 731-742 ◽  
Author(s):  
P. Wirfält ◽  
P. Stoica ◽  
G. Bouleux ◽  
M. Jansson
Keyword(s):  
Prior Knowledge ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Knowledge Based

scholarly journals Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Dual Frequency ◽  
Knowledge Based ◽  
Slot Size ◽  
Good Agreement

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

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