A new fuzzy neural network controller is designed in the winding machine.
在玻璃钢缠绕设备的控制系统中，设计了一种新型模糊神经网络控制器。
Presents a Fuzzy Neural Network Controller (FNNC) to multi capacity objects in flow control.
针对多容对象的流量控制，提出一种模糊神经网络控制器。
A design scheme of optimal RBF fuzzy neural network controller is proposed based on artificial immune principle.
提出了一种基于人工免疫原理的最优r BF模糊神经网络控制器设计方案。
Such fuzzy neural network controller is applied to the control of nickel hydrogen battery charging, have proved the validity of the algorithm.
将这种模糊神经网络控制器应用于镍氢电池的充电控制中，证明了算法的有效性。
DSP technology and fuzzy neural network structure presented in the paper pave the way for the engineering application of fuzzy neural network controller.
DSP技术以及本文所提出的模糊神经网络结构为模糊神经网络控制在工程中的应用开辟了一条新路。
From the points view from actual industrial use, the applications and design procedures of fuzzy neural network controller are researched in this dissertation.
从工程实际应用的角度出发，对模糊神经网络控制器的整个设计环节及应用过程进行了研究。
This paper introduces fuzzy neural network controller self organization with expert modulating tactics for the application of edible alcohol ferment temperature control.
简要介绍了具有专家调整策略的神经网络自组织模糊控制器在食用酒精发酵温度控制中的应用。
Simulation results show that the design of the fuzzy neural network controller can reduce the average delay of vehicles effectively, and meet the demand for real-time control.
仿真结果表明，本文设计的模糊神经网络控制器能够有效降低车辆平均延误，满足实时控制的要求。
The parameters of the fuzzy neural network controller are optimized by the mixed learning methods with BP algorithm and Simulated Annealing algorithm which improves BP algorithm.
该系统的控制器采用模糊神经网络控制器，它的控制器参数采用模拟退火算法全局优化来对BP算法进行改进的混合方法。
This paper USES robot as researched object that is of strong coupling, nonlinear and multi-variable characters. A control system is proposed which consists of a fuzzy neural network controller.
本文以机器人为研究对象，针对其强耦合、非线性、多变量等特点，提出了一种模糊神经网络控制器组成的控制系统。
Aiming at the design difficulty for fuzzy neural network controller, an immune evolutionary algorithm is proposed to design the parameters of a radial basis function fuzzy neural network controller.
针对模糊神经网络控制器难于设计的问题，提出了一种免疫进化算法用于径向基函数模糊神经网络控制器参数的优化设计。
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。
Fuzzy Neural Network combined with conventional PD controller is used for the kinematics control and a fuzzy rule extraction method is put forward.
采用模糊神经网络结合常规PD控制器的方法来进行机器人的运动控制，提出了提取模糊规则的方法。
Fuzzy logic and neural network controller and their applications in an electro hydraulic servo position system are discussed.
讨论了模糊逻辑和神经网络控制器在电液伺服位置系统中的应用。
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.
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。
For vector control AC drive system, the thesis presented a fuzzy neural network speed controller based on reinforcement learning.
针对矢量控制交流调速系统，该文提出并设计了一种基于再励学习的模糊神经网络速度控制器。
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.
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。
A direct inverse model controller of fuzzy neural network with changeable structure based on t s inference is presented in this paper and it is used to the motion control of mobile robot.
本文提出一种基于T -S模型的变结构模糊神经网络直接逆模型控制器，并将其应用于移动机器人的运动控制中。
Based on fuzzy neural network, this paper presents a self learning controller used to industrial kiln temperature system.
本文提出一种模糊神经网络自学习控制方法，并应用于窑炉温度控制系统中。
The emphasis is placed on the design of the fuzzy controller, the method of determining system′s gain parameter and the method of realizing BP neural network.
重点研究了模糊控制器的设计、系统增益参数的确定方法和BP神经网络的实现方法。
A novel indirect adaptive controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed for affine 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。
The parameters of me fuzzy control rules of me controller can be learned by the learning slgorithm of the neural netowrk. and the inference process can be realized by the network.
应用单层神经网络可以学习多变量模糊控制规则中的未知参数．还可由它来实现多变量模糊推理过程。
Then a fuzzy controller is designed based on relationship of input vs. output of electric characteristics. The neural network is trained from the samples of input and output of the fuzzy controller.
然后，基于电特性的输入输出关系设计了一个模糊控制器，且利用模糊控制器的输入输出样本训练神经网络。
Then a fuzzy controller is designed based on relationship of input vs. output of electric characteristics. The neural network is trained from the samples of input and output of the fuzzy controller.
然后，基于电特性的输入输出关系设计了一个模糊控制器，且利用模糊控制器的输入输出样本训练神经网络。
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