CELLULAR NEURAL NETWORK MODELS OF GROWTH AND IMMUNE OF EFFECTOR CELLS RESPONSE TO CANCER

2006 ◽  
Vol 17 (02) ◽  
pp. 223-233
Author(s):  
YONGMEI SU ◽  
LEQUAN MIN
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Reaction Diffusion ◽  
Cellular Neural Network ◽  
Neural Network Models ◽  
Effector Cells ◽  
Coupling Parameters ◽  
Immune Response To Cancer ◽  
Set Up

Four reaction-diffusion cellular neural network (R-D CNN) models are set up based on the differential equation models for the growths of effector cells and cancer cells, and the model of the immune response to cancer proposed by Allison et al. The CNN models have different reaction-diffusion coefficients and coupling parameters. The R-D CNN models may provide possible quantitative interpretations, and are good in agreement with the in vitro experiment data reported by Allison et al.

Dynamics of the brain at global and microscopic scales: Neural networks and the EEG

1996 ◽  
Vol 19 (2) ◽  
pp. 285-295 ◽  
Author(s):  
J. J. Wright ◽  
D. T. J. Liley
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Cellular Neural Network ◽  
Information Storage ◽  
Cortical Activation ◽  
Global Dynamics ◽  
Neural Network Models ◽  
Neuronal Interactions ◽  
Neural Interactions

AbstractThere is some complementarity of models for the origin of the electroencephalogram (EEG) and neural network models for information storage in brainlike systems. From the EEG models of Freeman, of Nunez, and of the authors' group we argue that the wavelike processes revealed in the EEG exhibit linear and near-equilibrium dynamics at macroscopic scale, despite extremely nonlinear – probably chaotic – dynamics at microscopic scale. Simulations of cortical neuronal interactions at global and microscopic scales are then presented. The simulations depend on anatomical and physiological estimates of synaptic densities, coupling symmetries, synaptic gain, dendritic time constants, and axonal delays. It is shown that the frequency content, wave velocities, frequency/wavenumber spectra and response to cortical activation of the electrocorticogram (ECoG) can be reproduced by a “lumped” simulation treating small cortical areas as single-function units. The corresponding cellular neural network simulation has properties that include those of attractor neural networks proposed by Amit and by Parisi. Within the simulations at both scales, sharp transitions occur between low and high cell firing rates. These transitions may form a basis for neural interactions across scale. To maintain overall cortical dynamics in the normal low firing-rate range, interactions between the cortex and the subcortical systems are required to prevent runaway global excitation. Thus, the interaction of cortex and subcortex via corticostriatal and related pathways may partly regulate global dynamics by a principle analogous to adiabatic control of artificial neural networks.

In vitro microemboli classification using neural network models and RF signals

Ultrasonics ◽  
2011 ◽  
Vol 51 (3) ◽  
pp. 247-252 ◽  
Author(s):  
N. Benoudjit ◽  
K. Ferroudji ◽  
M. Bahaz ◽  
A. Bouakaz
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Rf Signals

scholarly journals Existence and Global Exponential Stability of Equilibrium for Impulsive Cellular Neural Network Models with Piecewise Alternately Advanced and Retarded Argument

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Kuo-Shou Chiu
Keyword(s):  
Neural Network ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Sufficient Conditions ◽  
Network Models ◽  
Cellular Neural Network ◽  
Retarded Argument ◽  
Neural Network Models ◽  
Piecewise Constant Argument ◽  
Advanced Argument

We introduce impulsive cellular neural network models with piecewise alternately advanced and retarded argument (in short IDEPCA). The model with the advanced argument is system with strong anticipation. Some sufficient conditions are established for the existence and global exponential stability of a unique equilibrium. The approaches are based on employing Banach’s fixed point theorem and a new IDEPCA integral inequality of Gronwall type. The criteria given are easily verifiable, possess many adjustable parameters, and depend on impulses and piecewise constant argument deviations, which provides exibility for the design and analysis of cellular neural network models. Several numerical examples and simulations are also given to show the feasibility and effectiveness of our results.

Existence and global exponential stability of equilibrium for impulsive neural network models with generalized piecewise constant delay

2021 ◽  
pp. 2250001
Author(s):  
Kuo-Shou Chiu
Keyword(s):  
Neural Network ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Network Models ◽  
Cellular Neural Network ◽  
Impulsive Systems ◽  
Constant Delay ◽  
Neural Network Models ◽  
Piecewise Constant ◽  
Banach’S Fixed Point Theorem

In this paper, we investigate the models of the impulsive cellular neural network with generalized constant piecewise delay (IDEGPCD). To guarantee the existence, uniqueness and global exponential stability of the equilibrium state, several new adequate conditions are obtained, which extend the results of the previous literature. The method is based on utilizing Banach’s fixed point theorem and a new IDEGPCD’s Gronwall inequality. The criteria given are easy to check and when the impulsive effects do not affect, the results can be extracted from those of the non-impulsive systems. Typical numerical simulation examples are used to show the validity and effectiveness of the proposed results. We end the paper with a brief conclusion.

scholarly journals Analyzing stability of equilibrium points in impulsive neural network models involving generalized piecewise alternately advanced and retarded argument

2021 ◽  
Author(s):  
Kuo-Shou Chiu
Keyword(s):  
Neural Network ◽  
Global Exponential Stability ◽  
Equilibrium Points ◽  
Sufficient Conditions ◽  
Network Models ◽  
Cellular Neural Network ◽  
Retarded Argument ◽  
Impulsive Systems ◽  
Neural Network Models ◽  
Banach’S Fixed Point Theorem

In this paper, we investigate the models of the impulsive cellular neural network with piecewise alternately advanced and retarded argument of generalized argument (in short IDEPCAG). To ensure the existence, uniqueness and global exponential stability of the equilibrium state, several new sufficient conditions are obtained, which extend the results of the previous literature. The method is based on utilizing Banach’s fixed point theorem and a new IDEPCAG’s Gronwall inequality. The criteria given are easy to check and when the impulsive effects do not affect, the results can be extracted from those of the non-impulsive systems. Typical numerical simulation examples are used to show the validity and effectiveness of proposed results. We end the article with a brief conclusion.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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