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1)  FIR-TDNN
FIR时延神经网络
2)  Temporal Backpropagation
FIR神经网络
1.
The filter is based on FIR Neural Net- work using Temporal Backpropagation.
即利用输出数据对一种叫做基于实时反向传播算法的FIR神经网络进行训练,继而通过神经网络来预测输出数据,再与实际输出数据加权平均,从而提高了数据的准确性。
3)  Finite Impulse Response Neural Networks(FIRNN)
FIR神经网络(FIRNN)
4)  Time-Delay neural network
时延神经网络
1.
The paper applied neural network to the research of wood drying control, established time-delay neural network schedule model useful to wood moisture content prediction, gave the identification structure of time-delay neural network and used practical drying data of three species to train and test the networks.
将人工神经网络应用于木材干燥控制研究中,建立可用于木材含水率预测的时延神经网络基准模型,并给出其网络辨识结构。
2.
Based on the analysis of time-delay neural network and making use of the prior knowledge of character of discrete event dynamic system of military communication network, the uncertain time-delay neural network was put forward.
在分析了时延神经网络的基础上,利用军事通信网具有离散事件动态系统性质的先验知识,提出不确定时延神经网络。
5)  time-delay neural network
延时神经网络
1.
As a new data forecasting model,the double BP neural network combined model based on regressive neural network and time-delay neural network was proposed, which has the advantages of nonlinear combined forecasting methods.
在回归和延时神经网络的基础上,利用非线性组合预测方法的优点,提出一种新的预测模型——双重BP神经网络组合模型模型,选用某660MW机组的主蒸汽流量数据进行学习训练,实例计算结果表明双重BP神经网络组合模型可提高单项预测模型的精度,校核样本的平均相对误差为1。
2.
By using the principle of various spatial curve classification and using time-delay neural network,the time-delay neural network can distinguishthe little circuit response difference caused by the soft fault and can classify the circuit response properly within the circuit part s tolerance,so the network can be used in diagnosis of the of analog circuitsoft fault.
利用延时神经网络对不同空间曲线的分类原理,延时神经网络可以检查出软故障造成的响应信号之间的细微区别,并正确分类元件容差范围内的电路响应信号,具有诊断模拟电路软故障的能力。
6)  time delay neural network
时延神经网络
1.
The ability of neural networks to deal with time dynamic signal was improved with a new neural network architecture specializing in syllable recognition based on the time delay neural network (TDNN) and the convolutional neural network.
为了提高人工神经网络处理动态信号能力 ,在时延神经网络 ( TDNN )和卷积神经网络 ( CNN)的基础上 ,针对孤立音节的特点 ,提出了一个新的网络结构 ,研究了其学习算法。
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