针对空间目标的RCS特征识别的问题，提出了基于粒子群算法(PSO)训练的时延神经网络(TDNN)识别方法。
In order to identify the characteristic of the exo-atmospheric space target's RCS, the time-delay neural network (TDNN) with particle swarm optimization (PSO) training method is proposed.
还利用三种飞机缩比模型的暗室测量数据，研究了时延神经网络分类器中时延单元数目对分类精度的影响以及分类器的分类性能。
The effect of time delay unit number on classification precision and the performance of TDNN classifier using three typical aircraft dark room data measured with scale model were studied.
对于非线性，大时延、变参数的造纸过程灰份含量的控制，本文给出了一种神经网络控制方法。
In this paper, a neural network controller is designed for ash content control of papermaking process, neural networks are trained by using the output error of the controlled plant directly.
对于非线性，大时延、变参数的造纸过程灰份含量的控制，本文给出了一种神经网络控制方法。
In this paper, a neural network controller is designed for ash content control of papermaking process, neural networks are trained by using the output error of the controlled plant directly.
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