Approximate Solutions of the Michaelis–Menten Nonlinear Biochemical Reaction Model Using Sigmoid-Weighted Neural Networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Saeed Panahian Fard ◽  
Jafar Pouramini
Keyword(s):  
Neural Networks ◽  
Approximate Solutions ◽  
Reaction Model ◽  
Biochemical Reaction

Numerical Approximations to Solution of Biochemical Reaction Model

2011 ◽  
Vol 9 (1) ◽  
Author(s):  
Ahmet Yildirim ◽  
Ahmet Gökdogan ◽  
Mehmet Merdan
Keyword(s):  
Analytical Solution ◽  
Approximate Analytical Solution ◽  
Transformation Method ◽  
Reaction Model ◽  
Biochemical Reaction ◽  
Graphical Form ◽  
Kutta Method ◽  
Transform Method ◽  
Runge Kutta Method ◽  
Differential Transform

In this paper, approximate analytical solution of biochemical reaction model is used by the multi-step differential transform method (MsDTM) based on classical differential transformation method (DTM). Numerical results are compared to those obtained by the fourth-order Runge-Kutta method to illustrate the preciseness and effectiveness of the proposed method. Results are given explicit and graphical form.

scholarly journals Modelling a Biochemical Reaction with Computational Fluid Dynamics

2005 ◽  
Vol 3 (1) ◽  
Author(s):  
Sotos C. Generalis ◽  
Gregory M Cartland Glover
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Bubble Column ◽  
Reaction Model ◽  
Biochemical Reaction ◽  
Single Plane ◽  
Solids Concentration ◽  
Computational Fluid Dynamics Cfd ◽  
Specific Growth Rates ◽  
The Impact

Earlier investigations (Cartland Glover et al., 2004) into the use of computational fluid dynamics (CFD) for the modelling of gas-liquid and gas-liquid-solid flow allowed a simple biochemical reaction model to be implemented. A single plane mesh was used to represent the transport and reaction of molasses, the mould Aspergillus niger and citric acid in a bubble column with a height to diameter aspect ratio of 20:1. Two specific growth rates were used to examine the impact that biomass growth had on the local solids concentration and the effect this had on the local hydrodynamics of the bubble column.

Convergence of Numerical Solutions to Stochastic Delay Neural Networks with Poisson Jump and Markovian Switching

2011 ◽  
Vol 219-220 ◽  
pp. 1153-1157
Author(s):  
Hong Ge Yue ◽  
Qi Min Zhang
Keyword(s):  
Neural Networks ◽  
Numerical Methods ◽  
Numerical Solutions ◽  
Approximate Solutions ◽  
Markovian Switching ◽  
Explicit Solutions ◽  
Euler Scheme ◽  
Stochastic Delay ◽  
Local Lipschitz Condition ◽  
Poisson Jump

In general stochastic delay neural networks with Poisson jump and Markovian switching do not have explicit solutions. Appropriate numerical approximations, such as the Euler scheme, are therefore a vital tool in exploring their properties. Unfortunately, the numerical methods for stochastic delay neural networks with Poisson jump and Markovian switching (SDNNwPJMSs) have never been studied. In this paper we proved that the Euler approximate solutions will converge to the exact solutions for SDNNwPJMSs under local Lipschitz condition. This result is more general than what they deal with the Markovian switching term or the jump term.

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