Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture

2018 ◽  
Vol 146 ◽  
pp. 114-124 ◽  
Author(s):  
Chao Zhou ◽  
Kai Lin ◽  
Daming Xu ◽  
Lan Chen ◽  
Qiang Guo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Near Infrared ◽  
Fuzzy Model ◽  
Decision System ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Feeding Decision

scholarly journals Neuro-fuzzy model-based simulation of a laboratory scale clean-in-place system: A study of the rinsing process

2021 ◽  
Vol 4 ◽  
pp. 100098
Author(s):  
Rodrigo Sislian ◽  
Flávio V. da Silva ◽  
Marco A. Coghi ◽  
Rubens Gedraite
Keyword(s):  
Fuzzy Model ◽  
Laboratory Scale ◽  
Model Based ◽  
System A ◽  
Neuro Fuzzy

Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed

2016 ◽  
Vol 54 ◽  
pp. 162-169 ◽  
Author(s):  
Luis Virgen-Navarro ◽  
Enrique J. Herrera-López ◽  
Rosa I. Corona-González ◽  
Enrique Arriola-Guevara ◽  
Guadalupe M. Guatemala-Morales
Keyword(s):  
Digital Images ◽  
Fuzzy Model ◽  
Coffee Bean ◽  
Spouted Bed ◽  
Model Based ◽  
Neuro Fuzzy

scholarly journals Improvement of Urban Flood Alert Criteria Prediction Model based on Neuro-Fuzzy Initial Function and Training Data

2020 ◽  
Vol 20 (1) ◽  
pp. 327-337
Author(s):  
Hoseon Kang ◽  
Jaewoong Cho ◽  
Hanseung Lee ◽  
Jeonggeun Hwang
Keyword(s):  
Fuzzy Model ◽  
Training Data ◽  
Average Error ◽  
Urban Flood ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Flood Alert ◽  
Improved Model ◽  
Steep Slopes ◽  
And Training

In Korean metropolitan areas, the high density of residential and commercial buildings, highly impervious surfaces, and steep slopes contribute to floods that can occur within a short duration of heavy rainfall. To prepare for this, advance warning measures based on accurate flood alert criteria are needed. In our previous study, we demonstrated the applications of a Neuro-Fuzzy model that considersthe characteristics of the basin to predict flood alert criteria in areas with no damage. However, as the number of learning materials are low, at 27, the evaluation and verification of the model has not been sufficiently accomplished, and its application is limited. Therefore, in this study, we propose an improved model based on the initializing function of the Neuro-Fuzzy algorithm, the construction of training data, and preprocessing. Compared to the existing model, the improved model reduced the average error by 48.1%~65.4% and the RMSE by 50.7%~60.1%. The new model, when applied to actual floods, showed an improvement of 0.7%~19.1% in accuracy.

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