Multilayered neural network offers a new exciting alternative for modelling unknown nonlinear dynamical system.
神经网络为未知非线性动态系统的建模提供了一条新途径。
This paper presents a text categorization model based on multilayered feedforward neutral network, and introduces the design and implementation of this model.
给出一种基于多层前馈神经网络的中文文本分类模型，介绍了该模型的设计和实现。
A modified neural network structure which is composed of a linear network and a multilayered feedforward neural network (MFNN) is presented.
本文提出一种改进的神经网络结构，它由线性网络和多层前向网络两部分组成。
Based on the theorem of the existence of multilayered neural network mapping, a model of artificial neural network is set up for approximate structural analysis.
基于多层神经网络映射存在定理，建立近似结构分析的人工神经网络模型。
This paper investigates the identification of unknown nonlinear dynamical system using multilayered feedforward neural network with a single hidden layer.
本文探讨了只用单个隐含层的前向神经网络对未知非线性动态系统的识别。
The simulation results are presented to demonstrate that the model of an unknown nonlinear dynamical system is built with the multilayered feedforward neural network model.
仿真实例进一步表明，采用神经网络建立未知非线性动态系统的在线模型具有可行性。
In this paper, a novel fast learning algorithm for multilayered feedforward neural network is introduced.
本文提出一种前馈神经网络的快速学习算法。
Through microwave network decomposition principle, we divided multilayered microwave circuit into simple circuit of sub-structure to analyze.
本文通过微波网络分解，将复杂的多层微波电路分解为结构相对简单的子结构电路进行分析。
Through microwave network decomposition principle, we divided multilayered microwave circuit into simple circuit of sub-structure to analyze.
本文通过微波网络分解，将复杂的多层微波电路分解为结构相对简单的子结构电路进行分析。
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