讨论了基于自适应模糊神经网络的噪声抵消器的设计方法。
The adaptive Fuzzy Neural Network is employed for adaptive noise cancellation.
基于自适应模糊神经网络，提出一种新的特征信息融合算法。
The fuzzy neural network is analyzed. Based on the fuzzy neural network, a new attribute information fusion method is presented.
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.
使用减法聚类和自适应模糊神经网络方法设计了一种水下机器人运动规划器。
By using the methods of subtraction clustering and adaptive fuzzy neural network, a sort of motion planner for the underwater vehicle was designed.
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.
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.
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.
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.
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.
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.
利用灰色理论本身的特征对经济参数进行预测，并运用自适应模糊神经网络对其拟合误差进行预测，从而达到较好的预测效果。
This article utilizes the character of Gray Theory to predict the economy parameter, and then in order to obtain a better predicting effect, the ANFIS is employed to predict its simulating error.
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.
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.
本文根据变量泵的具体情况，为其设计了自适应的神经网络模糊控制器，实现了变量泵的智能控制。
This paper describes that the combination of neural networks and fuzzy controller make the designing an intelligent controller for variable piston pumps become feasible and realistic.
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.
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.
鉴于模糊神经网络具有良好的非线性特性、学习能力、自适应能力和抗干扰能力，本文将模糊神经网络技术引入到高速公路入口匝道控制中。
Due to the traits of nonlinear, capacity of study, adaptivity and anti-interference, neural-fuzzy network is suitable for the control of ramp metering.
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).
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.
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.
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.
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.
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.
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.
仿真结果表明，所设计的神经网络模糊控制器具有自学习、自适应等优点，达到了在线控制的目的。
The result of simulation shows that this neural network fuzzy controller features self-learning and self-adaptive capabilities, and the purpose of on-line control is accomplished.
为了获得滤除噪声和细节保留两方面更好的平衡，提出了一种自适应模糊聚类神经网络。
AFCN (adapted fuzzy cluster neural network) is introduced to keep up trade-off between noise attenuation and detail preservation in image processing.
为了获得滤除噪声和细节保留两方面更好的平衡，提出了一种自适应模糊聚类神经网络。
AFCN (adapted fuzzy cluster neural network) is introduced to keep up trade-off between noise attenuation and detail preservation in image processing.
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