Global and local neural network models in biotechnology: Application to different cultivation processes

Karim, M. Nazmul; Yoshida, Takemi; Rivera, S. L.; Saucedo, Victor M.; Eikens, B.; Oh, Gyuseop

doi:10.1016/s0922-338x(97)87318-7

Cited by 62 publications

(16 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 Process variables of this study case are dilution rate, D, dimensionless temperature value, θ, conversion, y, and the likes. After developing the mechanistic model, the value of the independent variable (input) that is dilution rate, D, is varied from D=10 to 90s-1.…”

Section: Feedforward Artificial Neural Network (Fann) Model Developmentmentioning

confidence: 99%

“…The neural networks, which offer a data-driven modeling approach, are a viable modeling paradigm that can be used for that purpose, having good generalization and predictions capabilities. 1,2 As we all know, Single Neural Network (SNN) has been dominating the neural network -world‖. Even though there are lots of advantages of SNN but there are also some limitations for SNN as well as some problems that can deteriorate neural network performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Selective combination in multiple neural networks prediction using independent component regression approach

Foon

Rahim²,

Ahmad

et al. 2017

Chem. Eng. Res. Bull.

View full text Add to dashboard Cite

Biological processes are highly nonlinear in nature and difficult to represent accurately by simple mathematical models. However, this problem can be solved by using neural network. Neural network is a prominent modeling tool especially when it comes to intricate process such as biological process. In this paper, a multiple single hidden layer with ten hidden neurons Feedforward Artificial Neural Network (FANN) was used to model the complex and dynamic relationships between the input (dilution rate, D) and outputs (conversion, y and dimensionless temperature value, θ) for the reactive biological process. Levenberg-Marquardt Backpropagation training method was used. The multiple neural networks predicted outputs were then combined through three different methods which area simple averaging, Principal Component Regression (PCR) and Independent Component Regression (ICR). Multiple neural networks which were created by the bootstrap approach help improved single neural network performance as well as the model robustness for nonlinear process modeling. Comparison was made between the three methods. The result showed that ICR is slightly superior between the three methods especially in noise level 1,2 and 3, however ICR slightly suffer in noise level 4 and 5. This is due to the independent component regression used the latent factors and non-Gaussian distribution of y and θ values for the combination.

show abstract

Section: Feedforward Artificial Neural Network (Fann) Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Selective combination in multiple neural networks prediction using independent component regression approach

Foon

Rahim²,

Ahmad

et al. 2017

Chem. Eng. Res. Bull.

View full text Add to dashboard Cite

show abstract

“…Commonly, most of biological interactions cannot be elucidated by simple stepwise algorithm or a precise formula, particularly when the data set is complex, noisy, vague, uncompleted, or formed by different kinds of data (Prasad and Gupta 2006;Gallego et al 2011). Many times, behavior of a biological system over a time period is difficult to understand and interpret; additionally, genetic and environmental factors also show biological responses (Karim et al 1997).…”

Section: Problems In Modeling Biological Processmentioning

confidence: 99%

Machine Learning Techniques in Plant Biology

Osama

Mishra

Somvanshi

2015

PlantOmics: The Omics of Plant Science

View full text Add to dashboard Cite

“…The performance and benefits of neural networks and genetic procedures for data analysis persuaded a lot of biotechnologist researchers to focus on adaptation and application of artificial learning methods in modelling, observation, control and optimization of agricultural processes [5,6,9,10,14].…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy concepts applied for planning of the cereal crops: applications to the maize hybrids growing in a Romanian region

Constantinescu

2016

Journal of Biological Dynamics

View full text Add to dashboard Cite

The aim of our study is to improve the crop planning procedures using neuro-fuzzy concepts. In this paper we design a neuro-fuzzy procedure that offers the suitable maize hybrid, from a set of preferred hybrids, which must be organically farmed in the current year. Our method is a statistical one, on the one hand it processes data provided by the previous years and on the other hand it takes in account the vague character of the environmental factors. Also we present here some experimental results obtained by us on a certain set of real data, results which prove the efficiency of our approach. ARTICLE HISTORY

show abstract

Global and local neural network models in biotechnology: Application to different cultivation processes

Cited by 62 publications

References 9 publications

Selective combination in multiple neural networks prediction using independent component regression approach

Selective combination in multiple neural networks prediction using independent component regression approach

Machine Learning Techniques in Plant Biology

Neuro-fuzzy concepts applied for planning of the cereal crops: applications to the maize hybrids growing in a Romanian region

Contact Info

Product

Resources

About