Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
Hidden layers of Artificial Neural Networks (ANN) have large effects on the speed, precision and convergence for training the ANN.
隐含层对人工神经元网络(ANN)训练的速度、精度和收敛性有很大的影响。
Aimed at the low self-learning ability drawbacks of traditional expert system, artificial neural networks(ANN) were applied in crop nutrition diagnosis system.
针对传统专家系统自学习能力差的缺点，设计了基于神经网络的作物营养诊断专家系统。
The Antibody Network (ABNET), which is a new Artificial Neural Networks (ANN) based on immune principle, has been proved to have good ability of unsupervised and competitive learning in experiments.
抗体网络作为一种新型的基于免疫原理的神经网络模型，已有实验验证了其具有良好的无监督竞争学习能力。
Artificial Neural Networks (ANN) have been studied for simplified simulation to the activation of human brain and vision.
人工神经网络(ANN)是人视觉和脑的基本功能的抽象、简化和模拟。
The training model of test simulation for car of inverted pendulum based on BP algorithm of artificial neural networks (ANN) is a BP network that has 4-input and 3-layer structure.
基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。
Artificial neural networks (ANN) can be used to simulate the visual thinking process of experts, and many of its advantages make itself have some natural connection and complementation with CBR.
人工神经网络(ANN)可以用来模仿专家的形象思维，它的许多优点使得CBR与ANN之间存在某种自然联系，在很多方面两者具有互补性。
This paper introduces an identification of sedimentary microfacies by the pattern recognition approach using artificial neural networks (ANN).
本文介绍用于沉积微相智能识别的神经网络模式识别方法。
The first part of this paper deals with the principles of artificial neural networks(ANN). The effects of various ANN parameters on prediction are discussed.
介绍了人工神经网络（ANN）原理，详细讨论了网络参数的选择及其对预报的影响。
The study on internal behaviors of ANN is meaningful for the understanding of both biological and artificial neural networks.
人工神经网络（ANN）内部行为的研究，无论是对生物神经系统内部工作机理、ANN理论，还是对ANN应用都有重要意义。
The study on internal behaviors of ANN is meaningful for the understanding of both biological and artificial neural networks.
人工神经网络（ANN）内部行为的研究，无论是对生物神经系统内部工作机理、ANN理论，还是对ANN应用都有重要意义。
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