1) feed forward neural networks
前馈神经元网络
1.
The reason of local optimization for learning multi layer feed forward neural networks is discussed.
介绍了拟牛顿公式中 BFGS修正算法和 Wolf- Powell不精确线性搜索准则所具有的全局收敛性质 ,分析了将该算法应用到前馈神经元网络的训练学习中存在局部最优的原
2) multiple layer forward neural network
多层前馈型神经元网络
3) feed-forward neural network
前馈式神经元网络
1.
6 simulation and as a phenomenological study,a feed-forward neural network with multiple output neurons is built to identify and separate different ed events from H→r+r-,Drell-Yan,tt and W+ W-processes in LHC pp collision.
６蒙特卡罗模拟，作为一种唯象的研究，对于ＬＨＣＰＰ对撞中来自Ｈ→ｒ＋ｒ－，Ｄｒｅｌｌ－Ｙａｎ，以及ｔｔ和Ｗ＋Ｗ－过程的ｅμ事例，用一具有多个输出网点的前馈式神经元网络进行鉴别，对各物理过程均获得了满意的选择效率和本底压低水平。
2.
As a phenomenological study based on Monte Carlo simulation, a feed-forward neural network is designed to reconstruct Higgs invariant mass for giving ocrred M" mass position and satisfactory width, as well as the background reje.
这里作为基于蒙特卡罗模拟的唯象性研究，设计了一个前馈式神经元网络，用以重建Ｈｉｇｇｓ粒子的不变质量，不仅得到了正确的ＭＨ质量峰值及比较好的宽度，并同时具有抗本底事例干扰能力，可应用于实验的共振态新粒子寻找及粒子质量的精确测量。
4) multi-layered forward neural network
多层前馈神经元网络
5) Feedforward Neural Network
前馈神经网络
1.
Multi-layer feedforward neural network based on binary ant colony algorithms;
基于二元蚁群算法的多层前馈神经网络
2.
Chaos BP hybrid learning algorithm for feedforward neural network;
前馈神经网络的混沌BP混合学习算法
3.
A new feedforward neural network pruning algorithm;
一种新的前馈神经网络删剪算法
6) feedforward neural networks
前馈神经网络
1.
Newton-gradient coupling algorithm for feedforward neural networks;
前馈神经网络的梯度-牛顿耦合学习算法
2.
Computing Lyapunov exponents with feedforward neural networks;
利用前馈神经网络计算Lyapunov指数
3.
,this paper proposes a new algorithm which combined the advantages of the momentum feedforward neural networks and the traditional CMA blind equalization algorithms,which adjusts the new weight value with the adjusting value used before so that the algorithm could be less sensitive to the stationary point of the error surface.
针对基于前馈神经网络的盲均衡算法中,BP优化算法具有收敛速度慢、易陷入局部极小的缺点,提出了一种新的盲均衡算法,该算法结合动量项前馈神经网络与传统恒模盲均衡算法的优点,将以前权值的调节量用于当前权值的修改过程,降低了算法对于误差曲面局部极值点的敏感性。
补充资料：神经元形态中枢神经系统内轴突髓鞘的形成示意图
李瑞端绘
[图]
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条