Convergence properties of an adaptive lattice filter exploiting nonlinear dynamics of a biochemical reaction system

1994 ◽  
Vol 12 (9) ◽  
pp. 1524-1529 ◽  
Author(s):  
Y. Iiguni
Keyword(s):  
Nonlinear Dynamics ◽  
Reaction System ◽  
Biochemical Reaction ◽  
Convergence Properties ◽  
Lattice Filter

Gram—Schmidt Neural Nets

1990 ◽  
Vol 2 (1) ◽  
pp. 116-126 ◽  
Author(s):  
Sophocles J. Orfanidis
Keyword(s):  
Network Structure ◽  
Fast Convergence ◽  
Neural Nets ◽  
Adaptive Array ◽  
Neural Net ◽  
Convergence Properties ◽  
Lattice Filter ◽  
New Type

A new type of feedforward multilayer neural net is proposed that exhibits fast convergence properties. It is defined by inserting a fast adaptive Gram-Schmidt preprocessor at each layer, followed by a conventional linear combiner-sigmoid part which is adapted by a fast version of the backpropagation rule. The resulting network structure is the multilayer generalization of the gradient adaptive lattice filter and the Gram-Schmidt adaptive array.

Algebraic Criteria for Qualitative Analysis of Nonlinear Dynamics in Biochemistry

2008 ◽  
Author(s):  
Kiriakos Kiriakidis ◽  
George Nakos
Keyword(s):  
Nonlinear Dynamics ◽  
Asymptotic Stability ◽  
Convex Hull ◽  
Qualitative Analysis ◽  
Differential Inclusions ◽  
Sufficient Conditions ◽  
Biochemical Reaction ◽  
Aggregate Modeling ◽  
Linear Differential Inclusions ◽  
Linear Differential

Aggregate modeling can approximate the convex hull of local matrices to nonlinear dynamics for any given accuracy. The authors use aggregate models to extend sufficient conditions for asymptotic stability of linear differential inclusions to nonlinear dynamics. An example illustrates the applicability of the proposed criteria to the analysis of nonlinear biochemical reaction chains.

Fabrication of a pen-shaped portable biochemical reaction system based on magnetic bead manipulation

2011 ◽  
Vol 21 (6) ◽  
pp. 067006 ◽  
Author(s):  
Mitsuhiro Shikida ◽  
Noriyuki Inagaki ◽  
Mina Okochi ◽  
Hiroyuki Honda ◽  
Kazuo Sato
Keyword(s):  
Reaction System ◽  
Magnetic Bead ◽  
Biochemical Reaction

scholarly journals Dynamic analysis of biochemical network using complex network method

2015 ◽  
Vol 19 (4) ◽  
pp. 1249-1253 ◽  
Author(s):  
Shuqiang Wang ◽  
Yanyan Shen ◽  
Jinxing Hu ◽  
Ning Li ◽  
Dewei Zeng
Keyword(s):  
Complex Network ◽  
Reaction System ◽  
Reaction Network ◽  
Reaction Model ◽  
Mass Action ◽  
Biochemical Reaction ◽  
Biochemical Network ◽  
Initial State ◽  
Complex Network Theory ◽  
Linear Differential

In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.

Nonlinear Dynamics in KIO4-SC(NH2)2-H2SO4 Reaction System

2002 ◽  
Vol 18 (03) ◽  
pp. 264-267
Author(s):  
Wang Shun ◽  
◽  
◽  
Lin Juan-Juan ◽  
Huang Zhen-Yan ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Reaction System
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