Learning algorithm for feedforward neural networks 前向神经网络学习算法研究
Simple conjugation-gradient bp algorithm for feedforward neural networks 前馈神经网络的一种简单共轭梯度学习算法
Nonlinear time-varying systems identification by feedforward neural networks 基于前向神经网络的非线性时变系统辨识
Identification of nonlinear time varying system using feedforward neural networks 利用前馈神经网络的非线性时变系统的辩识
Fuzzy cluster analysis method for determining the number of hidden nodes of feedforward neural networks 确定前向神经网络隐层节点数的模糊聚类分析法
feedforward neural network based on bp algorithm constructs identification of plant and inverse controller 我们采用基于bp算法的前馈神经网络构造对象辨识器和逆控制器。
Feedforward fuzzy neural network is the result of organically integrating fuzzy technology and feedforward neural network 前馈模糊神经网络是模糊技术与多层前馈神经网络有机结合的产物。
feedforward neural networks for modeling a nonlinear system are used to obtain its nonlinear model 利用前馈神经网络建立对象的非线性预测模型,在不同工作点做阶跃响应,建立其局部线性模型。
The new algorithm can be used to training other multi-layer feedforward neural networks, which can improve the generalization ability of them greatly 该算法可以推广应用于其它多层前向神经网络的训练中。
Based on weights analysis of feedforward neural networks, a hierarchic decomposition neural networks method for solving this problem is provided 基于前馈神经网络的权重分析,提出一种基于神经网络的结构优化层次分解方法,较好地解决了这一问题。