Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks

a b s t r a c tPower load forecasting is an essential tool for energy management systems. Accurate load forecasting supports power companies to make unit commitment decisions and schedule maintenance plans appropriately. In addition to minimizing the power generation costs, it is also important for the reliability of energy systems. This research study presents the implementation of a novel fuzzy wavelet neural network model on an hourly basis, and validates its performance on the prediction of electricity consumption of the power system of the Greek Island of Crete. In the proposed framework, a multiplication wavelet neural network has replaced the classic linear model, which usually appears in the consequent part of a neurofuzzy scheme, while subtractive clustering with the aid of the Expectation-Maximization algorithm is being utilized in the definition of fuzzy rules. The results related to the minimum and maximum load using metered data obtained from the power system of the Greek Island of Crete indicate that the proposed forecasting model provides significantly better forecasts, compared to conventional neural networks models applied on the same dataset.

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“…1 has been adopted from [22]. The multiplication wavelet neural network (LCW-MWNN) utilizes a modified version of the Morlet basis function.…”

Section: Wavelet Neural Networkmentioning

confidence: 99%

A hybrid intelligent approach for the prediction of electricity consumption

Amina

Kodogiannis

Petrounias

et al. 2012

International Journal of Electrical Power & Energy Systems

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“…Developing models from observed data is a fundamental problem in many fields, such as statistical data analysis, signal processing, control, forecasting, and computational intelligence [10]. Many improvements have been made in exploiting intelligent systems, some inspired by biological neural networks, fuzzy systems and integration of them [14].…”

Section: Neural Networkmentioning

confidence: 99%

“…To prioritize projects and satisfy both the investors and the society from benefitting from the projects, a mathematical tool which has the characteristics of prediction and evaluation is proposed in this study.If dependable forecasting model could be achieved, it will be very valuable for the assessment and selection of projects. Model development from observed data has been a primarysetback in varioussubjects, such as statistical data analysis, signal processing, control, forecasting, and computational intelligence [10]. In recent years, several soft computing methodslike neural networks, fuzzy inference systems, evolutionary computation, etc, and their hybrids have been successfully employed for developing predictive models to estimate the needed parameters [11].…”

Section: Introductionmentioning

confidence: 99%

Project Selection: Artificial Neural Network Approach

Akanni¹,

Adisa²,

Pule³

2013

SJBM

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To prioritize projects and satisfy both the investors and the society from benefitting from the projects, a mathematical tool which has the characteristics of prediction and evaluation is required. If a dependable forecasting model could be achieved, it will be very valuable for the assessment and selection of projects. This paper employs artificial neural network (ANN) technique in the selection of projects. To demonstrate this technique, the ANN modelis illustrated using Oral, Kettani and Lang's data on 37 R&D projects for its success. From the validation analysis, it was discovered that artificial neural network displayed a high potential to deciding how projects should be ranked and selected.

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“…; Amina et al . ). The radial basis function (RBF) network, as an alternative to the multilayer perceptron, has simpler structure, and suits for learning functions with local variations and discontinuities.…”

Section: Introductionmentioning

confidence: 97%

Prediction of the Growth Behavior of Aeromonas hydrophila Using a Novel Modeling Approach: Support Vector Machine

Liu

Guan

Schaffner

2014

Journal of Food Safety

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A new technique called support vector machine (SVM), which is used to predict the microbial growth, is presented in this paper. Experimental data on temperature, pH and NaCl from a previously published paper were modeled as inputs, and the kinetic growth parameters, including generation time (GT) and lag phase duration (LPD), were used as outputs of the SVM model. The results derived from SVM model, published artificial neural network (ANN) model and a traditional statistical model were compared using several evaluation criteria: accuracy factor (Af), bias factor (Bf), mean relative percentage residual, mean absolute relative residual, root mean square residual, internal validation (Q 2 ) and external validation (Q 2 ext). Graphical plots were also used for model comparison. The results show that SVM outperforms ANN and statistical model on predicting microbial GT and LPD, especially when predicting data not used for model development. Sensitivity analyses of the three environmental factors show that the most influential on LPD of Aeromonas hydrophila is temperature, followed by pH and NaCl, and the most influential on GT is pH, followed by temperature and then NaCl. PRACTICAL APPLICATIONSAeromonas hydrophila is a foodborne microorganism, which has been isolated from seafood, red meat, poultry, vegetables and water. Predictive microbiology describes microbial behavior using mathematical models to predict the microbiological safety and quality of a food. A nonlinear modeling technique, termed support vector machine (SVM), is proposed to predict lag phase duration and generation time of A. hydrophila. Statistical criteria show that SVM has a better predictive performance for the generation time and lag phase duration than neural network and traditional statistical models. Sensitivity analyses of the SVM can reveal which condition is the most influential factor on the lag phase duration and generation time, respectively. Therefore, SVM could become an alternative to other modeling approaches, all of which are faster than traditional microbiological experiments.those mathematical modeling methods to describe microbial behavior in order to prevent food spoilage as well as foodborne illness (Ross et al. 1999). The behavior of microbial populations in foods is determined by the properties of bs_bs_banner Journal of Food Safety

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Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks

Cited by 30 publications

References 50 publications

A hybrid intelligent approach for the prediction of electricity consumption

A hybrid intelligent approach for the prediction of electricity consumption

Project Selection: Artificial Neural Network Approach

Prediction of the Growth Behavior of Aeromonas hydrophila Using a Novel Modeling Approach: Support Vector Machine

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