采用自适应神经模糊推理系统,对控制器的参数进行优化。
The problem of rule explosion in FNN controller is solved for multi-variable systems.
根据自适应神经模糊推理系统原理,设计两个模糊系统分别逼近磁流变阻尼器的正模型和逆模型。
Then two different fuzzy systems are designed to approximate the direct model and the inverse one on the basis of adaptive neuro-fuzzy inference system(ANFIS).
作为一种局部逼近方法,自适应神经模糊推理系统(ANFIS)适于为药物定量构效关系(QSAR)建模。
Adaptive neural fuzzy inference system (ANFIS), as a local approximation approach, could be used to model the quantitative structure-activity relationship (QSAR) of medicine.
用小波分解和自适应神经模糊推理系统(ANFIS)相结合的方法,建立了赤道东太平洋海温的集成预报模型。
Based on the method of wavelet decomposition combining with ANFIS, a compositive prediction model of the equatorial east Pacific sea surface temperature anomaly (SSTa) was established.
针对纯碱碳化过程的复杂建模问题,提出基于T-S模型的自适应神经模糊推理系统(ANFIS)的建模方法。
The paper introduces a kind of adaptive neural-fuzzy inference systems (ANFIS) based on T-S model to deal with the modeling problem of the complex soda carbonization process.
首次提出了采用自适应神经模糊推理系统(ANFIS)建立全局负荷模型,描述电力负荷的非线性、变结构特性。
ANFIS (Adaptive Neural Fuzzy Inference System) is first presented to obtain the global load model for describing the nonlinear characteristics of the electric load in the paper.
针对目前制导炸弹命中精度低的问题,提出一种基于自适应神经模糊推理系统(ANFIS)的制导炸弹智能控制系统。
Aiming at the problem of guided bombs in low precision, this paper presents a kind of intelligence control system of guided bomb based on Adaptive Neuro-Fuzzy Inference system (ANFIS).
针对锌钡白干燥煅烧过程建模难的问题,提出了一种基于TS模型的自适应神经模糊推理系统(ANFIS)建模方法。
In order to solve the difficult modeling problem of the lithopone calcination process, this paper proposes an adaptive neural fuzzy inference systems (ANFIS) modeling method based on t s model.
同时基于自适应神经模糊推理系统建立了岩体力学参数与边坡抗滑力和下滑力的映射模型,分析得到抗滑力和下滑力的统计特征。
The model is used to perform the numerical simulation of slope stable state, to acquire the data for adaptive neuro-fuzzy inference system(ANFIS) analysis.
提出一种利用自适应神经模糊推理系统(ANF IS)技术有效解决磁流变(MR)阻尼器所固有的高度非线性动特性问题的方法。
An approach that can effectively solve the inherent highly nonlinear dynamics problem of magnetorheological (MR) damper is proposed by using adaptive neuro-fuzzy inference system (ANFIS) technique.
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.
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.
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.
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.
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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