1)neural network training神经网络训练
英文短句/例句
1.Application of ACO-BP in Neural Network TrainingACO-BP在神经网络训练中的研究与应用
2.Research on Some Real Questions in the Training of BPBP神经网络训练中的实际问题研究
3.Artificial neural networks training based on MCPSO algorithm基于MCPSO算法的BP神经网络训练
4.Influence of Neural Network Training Parameter etc on the Control Effect神经网络训练参数等对控制效果的影响
5.The Training Algorithm and Performance Study of Radial Basis Function Neural Network径向基神经网络训练算法及其性能研究
6.Application of immune particle swarm optimizer in neural network training免疫粒子群算法在神经网络训练中的应用
7.Algorithm to train feed-forward NN for approximately modeling针对近似建模的前馈神经网络训练算法
8.A Method to Choose Training Set of Mixed Chaos Neural Network一种混合混沌神经网络训练集的选择方法
9.The Research of BP Neural Network Training Based on the Chaos Ant Colony Optimization基于混沌蚁群算法的BP神经网络训练研究
10.Research on Training of Radial Basis Function Network Based on Kalman Filter Algorithm;基于卡尔曼滤波器算法的径向基神经网络训练算法研究
11.Training BP neural networks with ACO for identification of chaotic systems蚁群算法训练神经网络辨识混沌系统
12.Gradient algorithm has been widely used for training the weights of feedforward neural networks.梯度算法广泛应用于训练前馈神经网络.
13.Application of Neural Network Trained by Particle Swarm Optimization Algorithm;粒子群算法训练神经网络在教学中的应用
14.Research of Classification Ability and Training Algorithm of Feedforward Neural Network;前向神经网络的分类能力与训练算法的研究
15.Convergence Analysis of Gradient Algorithms for Training Higher-Order Neural Networks;高阶神经网络的梯度训练算法收敛性分析
16.APPLICATION OF BP-NEURAL-NETWORK TO PREDICT SWORD-TRAINING LOAD;利用人工神经网络分析预测击剑训练负荷量
17.Convergence Results of Gradient Algorithms for Training Feedforward Neural Networks前馈神经网络梯度训练算法的几个收敛性结果
18.Bee colony optimization algorithm for training feed-forward neural networks蜜蜂群优化算法用于训练前馈神经网络
相关短句/例句
input training neural network输入训练神经元网络
3)network training网络训练
4)network of training训练网络
5)training network网络训练
6)cyber war training网络战训练
1.Construction of a virtual target circumstances for cyber war training by honeypot technology;利用蜜罐技术架构网络战训练虚拟靶场环境
延伸阅读
神经网络训练分子式：CAS号：性质：指对人工神经网络训练。向网络足够多的样本，通过一定算法调整网络的结构（主要是调节权值），使网络的输出与预期值相符，这样的过程就是神经网络训练。根据学习环境中教师信号的差异，神经网络训练大致可分为二分割学习、输出值学习和无教师学习三种。
