1997
DOI: 10.1016/s0168-1605(96)01169-5
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Computational neural networks for predictive microbiology II. Application to microbial growth

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Cited by 91 publications
(36 citation statements)
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“…For further details on the principles of this method in the context of predictive microbiology, consult Hajmeer et al (1997), Hajmeer & Basher (2002.…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…For further details on the principles of this method in the context of predictive microbiology, consult Hajmeer et al (1997), Hajmeer & Basher (2002.…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…Their use is well known in chemical processes and they are a powerful tool for discovering relationships between sets of data. The artificial neural networks are also referred to as neurocomputers, connectionist networks, parallel distributed processors (Haykin, 1999) and computational neural networks (Sumpter and Noid, 1996;Hajmeer et al, 1997). This artificial intelligence method has attracted considerable attention because it can handle complex, nonlinear problems and requires less processing time than conventional methods.…”
Section: Neural Networkmentioning
confidence: 99%
“…Another study (Tomáz-Vért et al, 2000) demonstrated the possibility of using a simple neural network for discriminating antibacterial activity of compounds according to their structures with a high percentage of correct classifications. Neural networks developed for prediction of anaerobic growth of the bacterium S. flexneri on foods (predictive microbiology) have yielded better agreement with experimental data than have data from nonlinear regression equations (Hajmeer et al, 1997).…”
Section: Neural Networkmentioning
confidence: 99%
“…Nonlinearity is essential to the living microbial culture and limits strongly the use of traditional deterministic modeling techniques to represent the growth of microbes as a function of time [11]. Neural network have been engaged in recent years as an alternative to traditional regression models due to strength of describing complex and nonlinear problems [12].…”
Section: Introductionmentioning
confidence: 99%