提出基于自适应网络模糊推理系统(ANFIS)的神经模糊控制器作为船舶减摇鳍系统的控制装置。
In this paper, we present an Adaptive Network-based fuzzy Inference system (ANFIS), based on a neuro-fuzzy controller, as a possible control mechanism for a ship stabilizing fin system.
利用ANFIS较强的学习能力和模糊逻辑推理功能，建立了单桩竖向极限承载力预测的自适应网络模糊推理系统。
Based on the strong learning ability and fuzzy logic function of ANFIS, a method for predicting vertical ultimate bearing capacity of single pile is presented.
提出一种基于步态规划分级结构的自适应网络模糊推理系统控制策略，该方法不需要确定双足机器人运动学和动力学模型。
Proposed an adaptive network fuzzy inference system control strategy based on hierarchy structure of gait planning, which do not require detailed kinematics or dynamic biped models.
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.
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.
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.
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).
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.
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.
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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