Adaptively controlling algorithm based on neural network of multi-step prediction was proposed for the industrial processes.
针对工业过程的特点和控制要求，提出一种基于多步预测的神经网络自适应控制算法。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
针对交通流量混沌时间序列多步预测的问题，提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
Simulation res ults prove that this new multi-step prediction based on PID-like neural network control system can effectively attenuate random noise interference and is more robust and adaptive.
仿真实验表明，基于多步预测的PID型神经网络控制系统能有效抑制随机干扰，具有较强的适应性和鲁棒性。
The prediction model can take consideration of many factors′ effect on residual life and can make multi step prediction.
该模型能考虑多个因素对剩余寿命的影响，并能实现多步预报。
The prediction model can take consideration of many factors′ effect on residual life and can make multi step prediction.
该模型能考虑多个因素对剩余寿命的影响，并能实现多步预报。
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