Applying the genetic programming to modeling of diffusion processes by using the CNN and its applications to the synchronization
Applying the genetic programming to modeling of diffusion processes by using the CNN and its applications to the synchronization
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使用 CNN 将遗传编程应用于扩散过程建模及其在同步中的应用
DOI：
10.1002/ecjc.10064
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
S. Tokinaga
中科院分区：
文献类型：
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作者：
M. Yakabe;S. Tokinaga
CNN（细胞神经网络）被提出作为描述神经元与网络结构耦合的系统行为的方法。这便于分析经济社会决策风险扩散。然而，一般来说，只有在从观测数据推断出系统动力学之后，扩散分析才成为可能。本文利用GP（遗传编程）技术，提出了一种CNN动态估计方法。基于该建议，讨论了信号扩散并提出了一种控制传播的方法。为了用GP来逼近CNN中的系统方程，除了初等运算之外，还准备了分段线性函数等函数。通过这些和包含变量的树结构，定义了 GP 中的个体。在GP中，选择两个适应度较大的个体。 GP中的交叉处理是在适当确定的位置进行的，以便生成具有较高逼近函数能力的个体。在该方法中，表明近似是可能的，包括CNN的系统方程表现出混沌特性。接下来，采用利用估计的系统方程来估计CNN中信号的传播条件的方法。将结果与仿真结果进行比较，并讨论了计算方程的有效性。通过这种方式，可以估计截断网络上信号传播的扩散系数。此外，利用系统方程可用的事实，提出了一种用于使小区状态收敛到驻点或极限环的同步控制方法。在该方法中，通过测量的动态结果，可以估计反馈控制中的适当的控制输入，从而允许在更短的时间内达到平衡水平的控制。 © 2003 Wiley periodicals, Inc. Electron Comm Jpn Pt 3, 86(8): 19–30, 2003；在线发表于 Wiley InterScience (www.interscience.wiley.com)。 DOI 10.1002/ecjc.10064
The CNN (Cellular Neural Network) has been proposed as a method of describing the system behavior of neuron coupling with a network structure. This is convenient for analysis of decision risk diffusion in economic society. However, in general, analysis of diffusion becomes possible only after inference of the system dynamics from the observed data. In this paper, using the GP (Genetic Programming) technique, a method is proposed for estimation of dynamics in the CNN. Based on this proposal, the signal diffusion is discussed and a method is proposed for controlling propagation. In order to approximate the system equation in CNN by GP, functions such as piecewise linear ones are prepared in addition to elementary operations. With these and the tree structure containing variables, the individual in the GP is defined. In the GP, two individuals with large degrees of fitness are selected. Crossover processing in the GP is carried out at an appropriately determined place so that an individual with higher capability for approximating the function is generated. In this method, it is shown that approximation is possible, including the system equation of the CNN exhibiting chaotic characteristics. Next, a method of estimating the propagation condition of the signal in CNN by using the estimated system equation is adopted. The results are compared with those derived by simulation and the validity of the computational equation is discussed. In this way, the diffusion coefficient for truncating the signal propagation on the network can be estimated. Further, using the fact that the system equation is available, a control method for synchronization for convergence of the cell state to a stationary point or a limit cycle is proposed. In this method, by means of the measured dynamical results, an appropriate control input in the feedback control can be estimated, allowing control to a balanced level to be attained within a shorter time. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(8): 19–30, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10064