Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on del...Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.展开更多
Angiogenesis, the growth of new blood vessel from existing ones, is a pivotal stage in cancer development,and is an important target for cancer therapy. We develop a hybrid mathematical model to understand the mechani...Angiogenesis, the growth of new blood vessel from existing ones, is a pivotal stage in cancer development,and is an important target for cancer therapy. We develop a hybrid mathematical model to understand the mechanisms behind tumor-induced angiogenesis. This model describes uptake of Tumor Angiogenic Factor（TAF）at extracellular level, uses partial differential equation to describe the evolution of endothelial cell density including TAF induced proliferation, chemotaxis to TAF, and haptotaxis to extracellular matrix. In addition we also consider the phenomenon of blood perfusion in the micro-vessels. The model produces sprout formation with realistic morphological and dynamical features, including the so-called brush border effect, the dendritic branching and fusing of the capillary sprouts forming a vessel network. The model also demonstrates the effects of individual mechanisms in tumor angiogenesis： Chemotaxis to TAF is the key driving mechanisms for the extension of sprout cell; endothelial proliferation is not absolutely necessary for sprout extension; haptotaxis to Extra Cellular Matrix（ECM） gradient provides additional guidance to sprout extension, suggesting potential targets for anti-angiogenic therapies.展开更多
The application of cellular neural networks (CNN) for solving partial differential equations (PDEs) is investigated in this paper. Two kinds of the PDEs , the heat conduction equation and Poisson's ...The application of cellular neural networks (CNN) for solving partial differential equations (PDEs) is investigated in this paper. Two kinds of the PDEs , the heat conduction equation and Poisson's equation,are considered to be typical examples. They can be computed in real time by using the CNN ,while the CNN' s hardware is implemented by the integrated OP AMP . The experimental results show that the hardware performence is in agreement with that given by the computer simulation. Therefore,the CNN is a new powerful tool for solving PDEs.展开更多
In order to reduce the pressure of parameter selection and avoid trapping into the local optimum,a novel differential evolution( DE) algorithm without crossover rate is proposed. Through embedding cellular automata in...In order to reduce the pressure of parameter selection and avoid trapping into the local optimum,a novel differential evolution( DE) algorithm without crossover rate is proposed. Through embedding cellular automata into the DE algorithm,those interactions among vectors are restricted within cellular structure of neighbors while the cell own evolution,which may be used to balance the tradeoff between exploration and exploitation and then tune the selection pressure. And further more,the orthogonal crossover without crossover rate is used instead of the binomial crossover,which can maintain the population diversity and accelerate the convergence rate. Experimental studies are carried out on a suite of 7 bound-constrained numerical benchmark functions. The results show that the proposed algorithm has better capability of maintaining the population diversity and faster convergence than the classical differential evolution and several classic differential evolution variants.展开更多
文摘Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.
基金supported by the National Natural Science Foundation of China (No. 61070092)
文摘Angiogenesis, the growth of new blood vessel from existing ones, is a pivotal stage in cancer development,and is an important target for cancer therapy. We develop a hybrid mathematical model to understand the mechanisms behind tumor-induced angiogenesis. This model describes uptake of Tumor Angiogenic Factor（TAF）at extracellular level, uses partial differential equation to describe the evolution of endothelial cell density including TAF induced proliferation, chemotaxis to TAF, and haptotaxis to extracellular matrix. In addition we also consider the phenomenon of blood perfusion in the micro-vessels. The model produces sprout formation with realistic morphological and dynamical features, including the so-called brush border effect, the dendritic branching and fusing of the capillary sprouts forming a vessel network. The model also demonstrates the effects of individual mechanisms in tumor angiogenesis： Chemotaxis to TAF is the key driving mechanisms for the extension of sprout cell; endothelial proliferation is not absolutely necessary for sprout extension; haptotaxis to Extra Cellular Matrix（ECM） gradient provides additional guidance to sprout extension, suggesting potential targets for anti-angiogenic therapies.
文摘The application of cellular neural networks (CNN) for solving partial differential equations (PDEs) is investigated in this paper. Two kinds of the PDEs , the heat conduction equation and Poisson's equation,are considered to be typical examples. They can be computed in real time by using the CNN ,while the CNN' s hardware is implemented by the integrated OP AMP . The experimental results show that the hardware performence is in agreement with that given by the computer simulation. Therefore,the CNN is a new powerful tool for solving PDEs.
基金Supported by the National Natural Science Foundation of China(No.61501186)the Jiangxi Province Science Foundation(No.20171BAB202001)the Visiting Scholar Foundation of Jiangxi Province Young and Middle-aged University Teachers'Development Program([2016],No.169)
文摘In order to reduce the pressure of parameter selection and avoid trapping into the local optimum,a novel differential evolution( DE) algorithm without crossover rate is proposed. Through embedding cellular automata into the DE algorithm,those interactions among vectors are restricted within cellular structure of neighbors while the cell own evolution,which may be used to balance the tradeoff between exploration and exploitation and then tune the selection pressure. And further more,the orthogonal crossover without crossover rate is used instead of the binomial crossover,which can maintain the population diversity and accelerate the convergence rate. Experimental studies are carried out on a suite of 7 bound-constrained numerical benchmark functions. The results show that the proposed algorithm has better capability of maintaining the population diversity and faster convergence than the classical differential evolution and several classic differential evolution variants.
