该文提出一种用于复杂的非线性未知系统辨识的混合神经网络模型—自适应模糊神经网络(AFNN)。
This paper presents a compound neural network model, i. e., adaptive fuzzy neural network (AFNN), which can be used for identifying the complicated nonlinear system.
与多元线性回归、模糊回归和自适应模糊神经网络相比，该模型学习精度高且具有较好的泛化能力，能取得较好的预测效果。
Comparing with the models based on multiple statistic analysis, generalized regress-ion neural network or adapted fuzzy neural network model, it shows better learning precision and generalization.
针对液压弯辊系统数学模型的非线性、时变特性，本文设计了一种模糊神经网络模型参考自适应控制器。
In view of the time-variable and nonlinear characteristics of mathematical model of hydraulic bending roll system, this thesis design a new nonlinear adaptive controler based on fuzzy neural network.
提出了一种基于T - S模糊模型和自适应神经网络的跟踪控制方法。
A robust adaptive tracking control method is presented based on the fuzzy T-S model.
文中依据非线性舰船模型，应用模糊神经网络简化出适应于舵减横摇控制器设计的模糊线性模型，并设计了广义预测控制器。
According to the nonlinear model of ship, a predigested fuzzy linear model is built adapted to rudder roll stabilization using fuzzy neural networks.
由于预测中使用了一种基于高木-关野模糊模型的自适应模糊神经网络，从而使预测模型具有很强的自适应能力，预测结果也比较令人满意。
The prediction model has very strong self-adaptability because of using adaptive fuzzy neural network based on Sugeno-Tanaka fuzzy model, and the forecast result is also satisfactory.
通过一个非线性实例设计了它的自适应神经网络模糊模型，从仿真结果可看出改进后的非线性系统模型更有效。
By designing a self-adapt neural fuzzy model for a nonlinear system, we can draw a conclusion that the new nonlinear model has high precision and good visual effect.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
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