Modeling of wheat soaking using two artificial neural networks (MLP and RBF)

Kashaninejad, Mahdi; Dehghani, Amir Ahmad; Kashiri, Mahboobeh

doi:10.1016/j.jfoodeng.2008.10.012

Cited by 111 publications

(82 citation statements)

References 22 publications

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“…Based on the tests performed in this investigation, the authors found out that the application of activation functions with the arrangement of 3-19-2 for both of the responses in the Logarithm-sigmoid design and 3-16-16-2 and 3-22-2, respectively, in SG and WL in the Hyperbolic-tangent design exhibited the highest reliability. The results of this investigation are in agreement with the results obtained in some similar research including synthetic prediction of mass transfer in the process of osmotic dehydration of pumpkin and modelling of water absorption of wheat with perceptron neural network and radial basis function performed by (Kashaninejad et al, 2009;Mokhtarian and Shafafi Zenuzian, 2012). In other words, the interpretation of the figures ( Table 1).…”

Section: Resultssupporting

confidence: 90%

Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit

Jabrayili¹,

Farzaneh²,

Zare³

et al. 2016

International Agrophysics

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A b s t r a c t. Osmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y 1 ), osmotic solution temperature (y 2 ), and immersion time (y 3 ) were considered as the input parameters and solid gain value (x 1 ) and water loss value (x 2 ) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively.

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Section: Resultssupporting

confidence: 90%

Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit

Jabrayili¹,

Farzaneh²,

Zare³

et al. 2016

International Agrophysics

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“…First, is the number of hidden layers and second is the number of neurons in each hidden layer. Since almost all of the problems in neural network modeling could be solved with one hidden layer (Chen et al 2001;Kashaninejad et al 2008;Mohebbi et al 2007;Movagharnejad and Nikzad 2007;Ochoa-Martínez and Ayala-Apaonte 2007;Mitra et al 2009), an ANN with three layers was used in this research. In addition, using too many hidden layers may lead to problem of data overfitting, affecting the system's generalization capability (Abdullah et al 2006).…”

Section: Image Acquisition and Analysismentioning

confidence: 99%

Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit

Fathi

Mohebbi

Razavi

2009

Food Bioprocess Technol

132

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The objectives of this study were to use image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit slices. Kiwifruits were dehydrated implementing four different sucrose concentrations, at three processing temperatures and during four osmotic time periods. A multilayer neural network was developed by using the operation conditions as inputs to estimate water loss, solid gain, and color changes. It was found that artificial neural network with 16 neurons in hidden layer gives the best fitting with the experimental data, which made it possible to predict solid gain, water loss, and color changes with acceptable mean-squared errors (1.005, 2.312, and 2.137, respectively). These results show that artificial neural network could potentially be used to estimate mass transfer kinetics and color changes of dehydrated kiwifruit.

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“…Recentemente, novas técnicas, como redes neurais (Bucene & Rodrigues, 2004;Alvarez, 2009;Kashaninejad et al, 2009), sistemas de inferência nebulosos (fuzzy) (Carvalho et al, 2009), computação evolucionária e sistemas híbridos (Schultz & Wieland, 1997) têm sido empregadas para desenvolver modelos de predição e estimar parâmetros. Essas técnicas têm utilidade em diversas áreas de pesquisa, porque são adequadas para a análise de sistemas com incertezas, sendo usadas como alternativas aos métodos estatísticos (Yilmaz & Kaynar, 2011).…”

Section: Introductionunclassified

Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy

Silva¹,

Silva²,

Filho

et al. 2014

Rev. bras. eng. agríc. ambient.

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R E S U M OAtualmente, novas técnicas de processamento de dados, tais como redes neurais, lógica nebulosa (fuzzy) e sistemas híbridos, são utilizadas para elaborar modelos de predição em sistemas complexos e estimar parâmetros desejados. Neste artigo investigou-se a habilidade de se desenvolver um modelo de inferência adaptativo neuro fuzzy para estimação da produtividade de trigo utilizando-se uma base de dados da combinação dos seguintes tratamentos: cinco doses de N (0, 50, 100, 150 e 200 kg ha -1 ); três fontes (Entec, sulfato de amônio e ureia); duas épocas de aplicação de N (na semeadura ou em cobertura) e dois cultivares de trigo (E21 e IAC 370), avaliados durante dois anos, em Selvíria, MS. Através dos dados de entrada e saída o sistema de inferência neuro fuzzy adaptativo apreende e posteriormente pode estimar um novo valor de produção de trigo com base em doses diferenciadas de N. O erro de predição da produtividade de trigo em função das cinco doses de N, obtido com o sistema neuro fuzzy, foi menor que o valor obtido utilizando-se uma aproximação quadrática. Os resultados mostraram que o sistema neuro fuzzy é viável para desenvolver um modelo de predição visando estimar a produtividade de trigo em função da dose de N.Estimate of wheat grain yield as function of nitrogen fertilization using neuro fuzzy modeling A B S T R A C TCurrently new techniques for data processing, such as neural networks, fuzzy logic and hybrid systems are used to develop predictive models of complex systems and to estimate the desired parameters. In this article the use of an adaptive neuro fuzzy inference system was investigated to estimate the productivity of wheat, using a database of combination of the following treatments: five N doses (0, 50, 100, 150 and 200 kg ha -1 ), three sources (Entec, ammonium sulfate and urea), two application times of N (at sowing or at side-dressing) and two wheat cultivars (IAC 370 and E21), that were evaluated during two years in Selvíria, Mato Grosso do Sul, Brazil. Through the input and output data, the system of adaptive neuro fuzzy inference learns, and then can estimate a new value of wheat yield with different N doses. The productivity prediciton error of wheat in function of five N doses, using a neuro fuzzy system, was smaller than that one obtained with a quadratic approximation. The results show that the neuro fuzzy system is a viable prediction model for estimating the wheat yield in function of N doses. Palavras-chave: IntroduçãoAs condições de solo, clima e topografia, favoráveis ao cultivo de trigo, tanto de sequeiro como irrigado em épocas e altitudes definidas pela pesquisa, fazem do Brasil Central uma região de enorme potencial para a expansão desta cultura com a perspectiva de propiciar, a médio prazo, a autossuficiência na produção nacional. Outrossim, a inserção do trigo no Cerrado contribui para diversificar os sistemas produtivos regionais agregando elementos para a sustentabilidade de produção nesse ecossistema brasileiro (RCCBPT, 2005).A utilização de cultivares de tr...

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Modeling of wheat soaking using two artificial neural networks (MLP and RBF)

Cited by 111 publications

References 22 publications

Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit

Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit

Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit

Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy

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