当获得了足够的数据后，通过自适应神经网络模糊系统ANFIS来训练产生隶属度函数和模糊规则，即产生模糊控制器。
When obtaining plenty data, self-adapt neural network fuzzy control system ANFIS come into being subjection degree function and fuzzy rule, namely come into being fuzzy controller.
提出利用直接自适应模糊神经网络控制一类不确定非线性混沌系统新方法。
A novel direct adaptive fuzzy neural networks (FNNs) controller for a class of uncertain nonlinear chaotic system is presented.
基于模糊神经网络算法研究了非线性系统的噪声消除问题，设计了一类非线性自适应逆噪声消除控制器。
Based on Fuzzy Neural Network, the noise canceling problem of the nonlinear system was studied. A type of nonlinear adaptive noise controller was proposed.
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.
基于数据融合的思想，提出一种非线性系统的自适应神经网络模糊控制器的设计方法。
Based on data fusion method, an adaptive neuro-fuzzy controller of nonlinear systems is presented.
模糊神经网络系统可以根据系统输入输出信号，建立系统的输入输出关系，并对环境的变化具有较强的自适应学习能力。
Fuzzy Neural Network System (FNNS) can construct input? Output relationship by means of input and output signal and FNNS has special characteristics of adaptive learn while environment is changing.
该文应用自适应神经网络模糊推理系统的方法对一个典型系统进行建模仿真，并阐述这三个参数的寻优方法。
This paper gives the simulation example for modeling a typical system with Adaptive Neural-Fuzzy Inference system and expatiates the method for choosing these three parameters.
提出了一种自适应模糊小波神经网络的滑模控制策略，保证系统的跟踪误差和对外界干扰的抑制被衰减到期望的程度。
An adaptive sliding mode control based on fuzzy wavelet network is proposed to guarantee the effects of the tracking error and external disturbances can be attenuated to a specific attenuation level.
从质子交换膜燃料电池(PEMFC)实际应用的角度出发，应用自适应模糊神经网络技术对PEMFC系统进行建模与控制。
From practical application, adaptive fuzzy identification and control models of proton exchange membrane fuel cell (PEMFC) were developed based on input-output sampled data and experts' experience.
在此基础上，又设计了模糊神经网络预测控制器，实现了对非线性、大时滞系统高精度的自适应控制。
On the basis of this, a fuzzy-neural forecast controller is designed and robust adaptive control to the nonlinear big-lagged chaos system is realized.
本文对电动助力转向系统设计了自适应模糊神经网络控制器，仿真结果表明该控制器能较好提高汽车转向时的轻便性和灵敏性。
Besides, an adaptive neural fuzzy control method is proposed to control the system, simulation results show the control method can better improve the steering portability and sensitiveness.
提出一种新型的过热汽温控制方案，主控制器基于自适应神经网络模糊推理系统(ANFIS)进行设计。
A new superheated steam temperature control system design scheme is proposed, the main controller design is based on Adaptive Network-based Fuzzy Inference system (ANFIS).
自适应神经网络模糊推理系统(ANFIS)能基于数据建模，无须专家经验，自动产生模糊规则和调整隶属度函数。
Applying Adaptive Neural-Fuzzy Inference System (ANFIS) can produce fuzzy rules and adjust membership functions automatically based on data without experience of experts.
然后应用一种改进的模糊神经网络自适应控制系统，设计了TCBR的控制器。
Then, applying adaptive control system based on improved fuzzy neural network, a TCBR controller is designed.
讨论了一个基于神经网络处理系统，实现了推理知识的自动获取和自适应模糊推理，具有很强的实用性。
A practical neural networks based classification system was discussed in this paper, in which automatic knowledge acquiring and fuzzy reasoning was realized.
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.
仿真实验结果表明，具有自适应神经网络的模糊推理系统控制的异步电机矢量控制系统不仅动态和稳态性能都得到提高，而且具有较强的鲁棒性。
Simulation results show that the induction motor vector control system with adaptive neuro-fuzzy inference system can improve the static and dynamic performance of the motor and has good robust.
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。
An online adaptive fuzzy neural network identification and robust control approach were proposed for the adaptive control problem of SISO nonlinear system.
针对仿射非线性系统，提出了一种新型的基于动态递归模糊神经网络(DRFNN)的间接自适应控制器。
A novel indirect adaptive controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed for affine nonlinear system.
本文围绕非线性系统的模糊神经网络控制问题，展开研究，设计了一个自适应模糊神经网络控制系统。
This paper mainly focused on the problem of fuzzy neural network control of non-linear system and got into further study and then a self-adaptation control system of fuzzy neural network was designed.
本文还详细介绍了一种用多层前向神经网络实现模糊逻辑的自适应神经网络模糊推理系统——ANFIS，并用它来分析、验证神经模糊控制的控制效果。
This paper also stated the method of Adaptive Neural-Fuzzy Inference System (ANFIS) in details, which was used to analysis and testify effect of the NN-FC.
模糊神经网络是智能技术的一个重要分支，它是神经网络与模糊系统的有机结合，具有强大的自学习和自适应功能。
Fuzzy Neural network technology is one of the branches of intelligent technology. It is the combination of neutral network and fuzzy system, have the function of self-study and self-adaptive.
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.
并与国内外研究较多的无刷直流电机的基于自适应PID、模糊控制、神经网络控制、PID控制的双闭环控制系统进行仿真对比实验。
Simulation experiment compared with the double-loop motor subject to adaptive PID control, fuzzy control, neural-network control and conventional PID control is presented.
由于自适应模糊神经网络系统具有非线性映射和自学习能力，能够用于噪声信号的非线性建模。
The AFNNS has the abilities of nonlinear mapping and self-learning property and can be used to achieve the nonlinear model of the noise.
现在智能控制技术如模糊控制、神经网络控制技术应用广泛，可以提高系统的自适应性和可靠性。
The current control way about fuzzy control and NC—Neurocontrol can enhance capability of adaptability and dependability.
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.
阐述了在导弹系统存在不确定性情况下，基于自适应反演控制技术和模糊神经网络理论，提出了一种导弹滑模控制系统设计方法。
Based on adaptive backstepping control techniques and fuzzy-neural theory, a sliding mode control scheme is proposed for missile control systems with uncertainties.
阐述了在导弹系统存在不确定性情况下，基于自适应反演控制技术和模糊神经网络理论，提出了一种导弹滑模控制系统设计方法。
Based on adaptive backstepping control techniques and fuzzy-neural theory, a sliding mode control scheme is proposed for missile control systems with uncertainties.
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