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。
This paper presents an artificial neural network(ANN) approach to determine the time delay for a heating, ventilating, and air -conditioning(HVAC)plant to respond to control actions.
本文提出一种人工神经网络（ANN）方法，确定暖通空调（HVAC）系统对控制信号响应的时间迟延，在研究中选用了四层网络。
Based on nonlinear prediction ideas of reconstructing phase space, this paper presents a time delay BP neural network model, whose generalization is improved utilizing Bayes' regularization.
基于相空间重构的非线性预报思想，建立一个时滞的BP神经网络模型，采用贝叶斯正则化方法提高BP网络的泛化能力。
It is composed of three elements: PCA, time-delay neural network and model updating, where the offline model is trained through the algorithm GABP.
该方法由三部分组成：主元分析pca、时间延迟神经网络、软测量模型的在线校正。
Design for robust controller of uncertain neural network with time-varying delay was analyzed.
对不确定变时滞神经网络系统的鲁棒控制器的设计进行了分析。
Based on identifying the time-delay system of HVAC, the paper researches the linear and nonlinear systems identification methods with MATLAB system identification toolbox and neural network theory.
运用MATLAB辨识工具箱和神经网络理论，通过对暖通空调系统中常见的时滞对象的辨识，研究了基于神经网络的线性和非线性的辨识方法。
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 problem of exponential stability and robust stability for a class of discrete-time neural network with time-varying delay is investigated.
研究了一类时滞离散神经网络指数稳定及鲁棒稳定问题。
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。
Simulation results show that the design of the fuzzy neural network controller can reduce the average delay of vehicles effectively, and meet the demand for real-time control.
仿真结果表明，本文设计的模糊神经网络控制器能够有效降低车辆平均延误，满足实时控制的要求。
Simulation results show that the design of the fuzzy neural network controller can reduce the average delay of vehicles effectively, and meet the demand for real-time control.
仿真结果表明，本文设计的模糊神经网络控制器能够有效降低车辆平均延误，满足实时控制的要求。
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