A new multi-branch time delay neural network is adopted to conduct prediction research on chaotic time series.
采用新型多重分支时间延迟神经网络进行混沌时间序列预测研究。
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.
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。
The Adaptive Time-delay Neural Network (ATNN) based on exo-atmospheric space point target IR radiation sequences is proposed in order to improve the recognition rate.
为提高目标的正确识别率，提出一种基于空间点目标红外辐射序列的自适应时延神经网络(atnn)识别方法。
The Adaptive Time-delay Neural Network (ATNN) based on exo-atmospheric space point target IR radiation sequences is proposed in order to improve the recognition rate.
为提高目标的正确识别率，提出一种基于空间点目标红外辐射序列的自适应时延神经网络(atnn)识别方法。
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