The application of neural network identification techniques in ship control became an important research area in recent years.
将神经网络辨识技术应用于船舶运动控制成为近年来研究的一个重要方向。
Two kinds of methods of system identification which are least squares identification and neural network identification are adopted here.
系统辨识采用了最小二乘辨识和神经网络辨识两种方法。
An online adaptive fuzzy neural network identification and robust control approach were proposed for the adaptive control problem of SISO nonlinear system.
针对单输入单输出非线性系统的自适应控制问题，提出了一种在线自适应模糊神经网络辨识与鲁棒控制的方法。
Simulation results indicate that the modeling method by using the RBF neural network identification technique is effective with the established model featuring a relative high precision.
仿真结果表明采用RBF神经网络辨识建模的方法是有效的，建立的模型精度较高。
Propose wavelet packet decomposition frequency-band monitoring method, diagnose typical failures of rolling bearing, and extract eigenvector to prepare follow-up Neural network identification.
提出了基于小波包分解频带能量监测法，对滚动轴承的几种典型故障进行了诊断，并且提取特征向量，为后续的神经网络识别作准备。
Propose wavelet packet decomposition frequency-band monitoring method, diagnose typical failures of rolling bearing, and extract eigenvector to prepare follow-up Neural network identification.
提出了基于小波包分解频带能量监测法，对滚动轴承的几种典型故障进行了诊断，并且提取特征向量，为后续的神经网络识别作准备。
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