迭代函数系(iterated function system,IFS)是产生分形的一种非常有用的方法.一个IFS通常是由完备度量空间上的一组压缩映射构成,它的吸引子一般是分形.在经典的Kannan映射和广义K映射的基础上,引入了一类广义K迭代函数系(K-IFS).证明...迭代函数系(iterated function system,IFS)是产生分形的一种非常有用的方法.一个IFS通常是由完备度量空间上的一组压缩映射构成,它的吸引子一般是分形.在经典的Kannan映射和广义K映射的基础上,引入了一类广义K迭代函数系(K-IFS).证明了这类广义K-IFS存在唯一的吸引子,给出了广义K-IFS的吸引子的拼贴定理,构造了一个用广义K-IFS的吸引子逼近给定紧集的例子.展开更多
The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is n...The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.展开更多
文摘迭代函数系(iterated function system,IFS)是产生分形的一种非常有用的方法.一个IFS通常是由完备度量空间上的一组压缩映射构成,它的吸引子一般是分形.在经典的Kannan映射和广义K映射的基础上,引入了一类广义K迭代函数系(K-IFS).证明了这类广义K-IFS存在唯一的吸引子,给出了广义K-IFS的吸引子的拼贴定理,构造了一个用广义K-IFS的吸引子逼近给定紧集的例子.
文摘The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.
