With a large amount of real traffic data collected from the actual network , a nonlinear network traffic model based on artificial neural network ( ann ) theory was constructed to predict the network traffic 根据实际网络中测量的大量网络流量数据,建立一个时间相关的基于神经网络的流量模型,预测和分析网络流量状况。
The main researches in this paper are as follows : 1 improve the performance of classical xin " anjiang model . 2 theory of artificial neural network ( ann ) is introduced into runoff forecasting of shuicehng reserior 本文的工作主要有以下几方面:一、对传统预报模型进行改进二、将人工神经网络引入到水城水库洪水预报三、程序实现
As such in this study , attempt was made at coupling the artificial neural network ( ann ) with the xinanjiang conceptual model with the view to enhancing the quality of its flow forecast 鉴于此种情况本文将人工神经网络算法同新安江模型相耦合以提高模型预报的精度。计算中使用了近期观测资料以及模型中径流流量预报产生的误差残差。
An artificial neural network ( ann ) is adopted for structural reliability investigation and the relative problems to the ann model estabolishment and its training are discussed . the examples are also provided 摘要将人工神经网络应用于结构可靠性的研究,就结构可靠性研究的神经网络模型的建立及网络训练的有关问题进行了探索和讨论,同时给出了相应的算例。
The artificial neural networks ( ann ) with back propagation algorithms coupled with the sequential pseudo - uniform design ( spud ) was applied and demonstrated successfully to the modeling of the pmr system using limited but adequate experimental data 我们采用接续式拟均匀设计来安排实验取得少量但充足的数据并以类神经网路来建构钯膜反应器之代表性模式。
For the nonlinear and uncertain characteristic of hydraulic turbine power set , this paper combines the artificial neural networks ( ann ) with adrc theory and adjusts the structure of the controller according to the characteristic of the subject 针对水轮发电机组的非线性和不确定性,把神经网络与自抗扰控制理论相结合,并依据对象特性对控制器做了结构调整。
In previously decades , due to development of neurophysiology , methods have been developed to realize the information processes in brain , such as fuzzy logic ( fl ) , artificial neural networks ( ann ) , chaos and so on 为了更好的理解人脑信息处理的过程,我们有必要以人工的方法模拟人脑的某些功能。这些方法主要包括:模糊逻辑、人工神经网络和混沌动力学等等。
Compared with conventional statistic classifier , the artificial neural network ( ann ) has been developed and applied to remote sensing data classification problem , which does n ' t need suppose parameterized distribution of sample space in advance 与传统统计方法的分类器相比较,人工神经网络法不需要预先假设样本空间的参数化统计分布,正在被越来越普遍的应用于遥感图像分类的研究。
We make use of artificial neural network ( ann ) to model urban traffic system which is a heavily nonlinear , stochastic , time - variant and uncertain system . moreover we design the structure and learning arithmetic of ann 考虑到城市交通系统本身是一个具有严重非线性,随机性,时变性、不确定性的复杂系统,利用人工神经网络技术来建立城市交通流模型,并详细地阐述了神经网络的结构和学习算法。
Artificial neural network ( ann ) is a information processing system which is composed of a number of nonlinear computational elements which operate in parallel and are arranged in a manner reminiscent of biological neural interconnection 人工神经网络( ann : artificialneuralnetwork )是一种信号处理系统,它是由许多的具有非线性计算能力的计算单元模仿生物神经系统神经元的连接方式连接而成,以并行方式运作。