神经网络有几种不同的拓扑结构，但是最简单的一种是前馈网络。
Neural networks have several different topologies, but the simplest is known as a feedforward network.
提出一种基于系统结构的拟神经网络建模与优化原理及拓扑结构。
A novel principium and topology of modeling and optimizing is introduced, which employs like-neural networks based on system architecture.
径向基函数神经网络是一种拓扑结构简单、学习过程透明的神经网络模型。
Radial Basis Function Neural Network is a kind of Neural Networks which have simple topological structure and clear learn procedure.
提出的自适应粒子群优化算法，用于优化多层前馈神经网络的拓扑结构，提高了神经网络的学习质量和速度。
The structure of multi-layer feedback forward neural network is optimized by improved PSO. Learning quality and training speed of the neural network are improved.
大体上说，所有的人工神经网络都有相似的拓扑结构。
Basically, all artificial neural networks have a similar structure of topology.
采用改进的BP算法来训练神经网络，并对网络的拓扑结构及其训练参数的选择进行了分析。
It is used modified BP algorithm to train ANN, and analyze topology of ANN and ways how to select its train parameters.
混沌是一种现象和行为，而神经网络是一种特定计算模式的拓扑结构，它们有自身的特征，但也有共同的规律，即非线性动力学特性。
Chaos is a kind of phenomenon and behavior, and neural network is a kind of topology with specific compute mode. They have their own characters and similar nonlinear characters.
然后介绍了BP神经网络的基本原理，拓扑结构和映射关系；分析了BP神经网络的训练算法及算法构成思想。
Then it introduces the basic principle of BP neural network, topology structure, and the mapping relationship, analyses the training algorithm of BP neural network and its ideas.
和Hopfield模型不同的是 ，这种神经网络增加了一个隐含层来扩大网络的存储容量 ，并采用局部相连的拓扑结构来代替全相连 ，从而减少了计算复杂度 。
And the structure of this model takes the form of local-connection instead of full-connection in order to reduce the complexity of computation.
和Hopfield模型不同的是 ，这种神经网络增加了一个隐含层来扩大网络的存储容量 ，并采用局部相连的拓扑结构来代替全相连 ，从而减少了计算复杂度 。
And the structure of this model takes the form of local-connection instead of full-connection in order to reduce the complexity of computation.
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