Neuro-Fuzzy Systems Modeling Tools for Bacterial Growth

Author(s):  
Emad A. El-Sebakhy ◽  
I. Raharja ◽  
S. Adem ◽  
Y. Khaeruzzaman
Author(s):  
Renata Bernardes ◽  
Bruno Luiz Pereira ◽  
Felipe Machini Malachias Marques ◽  
Roberto Mendes Finzi Neto

2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Pedro D. Gaspar ◽  
Joel Alves ◽  
Pedro Pinto

Currently, we assist the emergence of sensors and low-cost information and communication technologies applied to food products, in order to improve food safety and quality along the food chain. Thus, it is relevant to implement predictive mathematical modeling tools in order to predict changes in the food quality and allow decision-making for expiration dates. To perform that, the Baranyi and Roberts model and the online tool Combined Database for Predictive Microbiology (Combase) were used to determine the factors that define the growth of different bacteria. These factors applied to the equation that determines the maximum specific growth rate establish a relation between the bacterial growth and the intrinsic and extrinsic factors that define the bacteria environment. These models may be programmed in low-cost wireless biochemical sensor devices applied to packaging and food supply chains to promote food safety and quality through real time traceability.


2012 ◽  
Vol 05 (07) ◽  
pp. 477-482 ◽  
Author(s):  
Rafik Mahdaoui ◽  
Leila Hayet Mouss

Author(s):  
Julia Tholath Jose ◽  
Adhir Baran Chattopadhyay

Doubly fed Induction Generators (DFIGs) are quite common in wind energy conversion systems because of their variable speed nature and the lower rating of converters. Magnetic flux saturation in the DFIG significantly affect its behavior during transient conditions such as voltage sag, sudden change in input power and short circuit. The effect of including saturation in the DFIG modeling is significant in determining the transient performance of the generator after a disturbance. To include magnetic saturation in DFIG model, an accurate representation of the magnetization characteristics is inevitable. This paper presents a qualitative modeling for magnetization characteristics of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy systems with one hidden layer of Gaussian nodes are capable of approximating continuous functions with arbitrary precision. The results obtained are compared with magnetization characteristics obtained using discrete fourier transform, polynomial and exponential curve fitting. The error analysis is also done to show the effectiveness of the neuro fuzzy modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast learning convergence is observed and great performance in accuracy is achieved.


2007 ◽  
Vol 20 (2) ◽  
pp. 239-247 ◽  
Author(s):  
Xiao-kang Su ◽  
Guang-ming Zeng ◽  
Guo-he Huang ◽  
Jian-bing Li ◽  
Jie Liang ◽  
...  

Author(s):  
M. Korytkowski ◽  
R. Nowicki ◽  
L. Rutkowski ◽  
R. Scherer
Keyword(s):  

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