Introduction to Neural Networks

Baughman, D.R.; Liu, Y.A.

doi:10.1016/b978-0-12-083030-5.50007-2

Cited by 52 publications

(77 citation statements)

References 5 publications

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“…This artificial intelligence method has attracted considerable attention because it can handle complex, nonlinear problems and requires less processing time than conventional methods. Some authors have declared neural networks to be as one of the greatest computational tools ever developed (Baughman and Liu, 1995).…”

Section: Neural Networkmentioning

confidence: 99%

Evaluation of the effect of dry-film biocides on paint film preservation using neural networks

Contant

Junior

Machado

et al. 2010

Braz. J. Chem. Eng.

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-Biocides play an important role in the preservation of a variety of products susceptible to microbiological growth such as paint, a material that can undergo microbial deterioration both in storing (inside the can) and after the application on a surface. In this work, artificial neural networks were used to predict the level of fungal growth on surfaces painted with water-based paints with biocide formulations containing different concentrations of ten kinds of commercial and experimental chemical agents. The use of neural networks is well known in chemical processes and they are a powerful tool for discovering relationships between sets of data. Industrial Environmental Tropical Chamber tests were used as the network training set. The importance of the each additive of the dry-film biocide formulation in prevention of biodeterioration was also examined.

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Section: Neural Networkmentioning

confidence: 99%

Evaluation of the effect of dry-film biocides on paint film preservation using neural networks

Contant

Junior

Machado

et al. 2010

Braz. J. Chem. Eng.

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“…This a classic ANN with an input layer (first nodes at the bottom of the pyramid), hidden layer (a number of nodes making up the body of the pyramid, (which is typically 3, as it is sufficient for minimizing time and capable of handling the complexity for pattern recognition) and the output layer (last layer, top of the pyramid) which outputs the data Some parameters that are used to alter the performance of a ANN based on the needs are learning rate and momentum coefficient, transfer function, weight distribution factor, normalization of the input and output data sets and the number of hidden layers. 13 Normalization of the network data is done to level the significance across all variables and place them on an even scale. The learning rate of the ANN controls the acceleration of change within the weights.…”

Section: Figure 132: General Ann Architecturementioning

confidence: 99%

Temporal responses of chemically diverse sensor arrays for machine olfaction using artificial intelligence

Ryman

Bruce

Freund

2016

Sensors and Actuators B: Chemical

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The human olfactory system can classify new odors in a dynamic environment with varying odor complexity and concentration, while simultaneously reducing the influence of stable background odors. Replication of this capability has remained an active area of research over the past 3 decades and has great potential to advance medical diagnostics, environmental monitoring and industrial monitoring, among others. New methods for rapid dynamic temporal evaluation of chemical sensor arrays for the monitoring of analytes is explored in this work. One such method is high and low bandpass filtering of changing sensor responses; this is applied to reduce the effects of background noise and sensor drift over time. Processed sensor array responses, coupled

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“…They have been intensively used in different activity domains: banking, insurance, defence, industry, etc (Desheng et al, 2006;TaiYue and Shih-Chien, 2006;Pramanik, 2004;Shene et al, 1998Shene et al, , 1999Tseng-Chung and Li-Chiu, 2005). In industry, their applications are innumerable and exponentially increasing (Baughman and Liu, 1995); those specifically related to the drying process are mentioned by Lavric (1994, 1995). Nevertheless, no work has been reported concerning application of artificial neural networks to the modelling of the industrial process of drying grated coconut.…”

Section: Introductionmentioning

confidence: 99%

Modeling of an industrial drying process by artificial neural networks

Assidjo

Yao

Kisselmina

et al. 2008

Braz. J. Chem. Eng.

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-A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN), precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.

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Introduction to Neural Networks

Cited by 52 publications

References 5 publications

Evaluation of the effect of dry-film biocides on paint film preservation using neural networks

Evaluation of the effect of dry-film biocides on paint film preservation using neural networks

Temporal responses of chemically diverse sensor arrays for machine olfaction using artificial intelligence

Modeling of an industrial drying process by artificial neural networks

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