自适应神经网络模糊系统,ANFIS
1)ANFIS自适应神经网络模糊系统
1.This paper first introduces the general principles ofANFIS, and then gives a practical application on the platform of MATLAB.本文首先介绍了自适应神经网络模糊系统,然后在MATLAB平台上给出了它在信号处理中的应用,结果证明其确实有效。
2.In order to study the influence of oxygen sensor deterioration on gasoline engine emission and catalytic converter effenciency,an emission model of gasoline engine was developed by combining Radial Basis Function(RBF) neural network with Adaptive Neural Fuzzy Inference System(ANFIS).针对氧传感器对汽油机排放和催化转化器效率的影响,建立了径向基函数网络(RBFNN)和自适应神经网络模糊系统(ANFIS)相结合的汽油机排放模型。
2)ANFIS自适应神经网络模糊推理系统
1.Adequate datas used to train fuzzy rules and membership function in the BP neural network are obtained by means of LQR algorithm,thenANFIS controller is designed,in the end,a building is taken as the example to simulink under El Centro earthquake,the results shows thatANFIS is efficient in the structural active control.运用线性二次型经典最优控制算法获得学习样本,由神经网络反向传播算法训练产生模糊规则和隶属度函数,设计自适应神经网络模糊控制器,通过某结构地震波作用下振动控制的数字仿真,表明自适应神经网络模糊推理系统可以有效地应用到结构控制中。
2.Adaptive Neuro-Fuzzy Inference System(ANFIS) is a neural network system with one input and several outputs, which is got through achieving one step sugeno fuzzy system by the format of network.自适应神经网络模糊推理系统(AdaptiveNeuro FuzzyInferenceSystem, ANFIS)是将Sugeno一阶模糊系统以网络的形式来实现而得到的一种多输入单输出的神经网络。
3.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).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。
3)adaptive neural network-based fuzzy inference system自适应神经网络的模糊推理系统
1.Based on captured objective input-output data couple, a new ANFIS (adaptive neural network-based fuzzy inference system) framework, which can construct fuzzy membership function and fuzzy rules in Takagi-Sugeno fuzzy model, is used to model and control the complex inverted.该种方法采用BP算法与梯度下降法结合的混合算法对Takagi-Sugeno模糊模型中的前项及后件参数进行优化修正,在已获得的客观输入输出数据对的基础上,提出一种基于自适应神经网络的模糊推理系统ANFIS来对倒立摆系统进行建模和控制。
4)adaptive neural networks-fuzzy inference system自适应神经网络-模糊推理系统
1.A data fusion algorithm based onadaptive neural networks-fuzzy inference system(ANFIS) and D-S evidence theory is proposed.提出了一种基于自适应神经网络-模糊推理系统(ANFIS)和D-S证据理论相结合的信息融合方法。
5)adaptive fuzzy neural network自适应模糊神经网络
1.Studies on prediction of standard entropy of cations by usingadaptive fuzzy neural network;自适应模糊神经网络预测阳离子标准熵的研究
2.Studies on prediction of the stabilities of metal-HEDTA complex by usingadaptive fuzzy neural network;自适应模糊神经网络预测金属-HEDTA配合物稳定常数的研究
英文短句/例句
1.Studies on Adaptive Fuzzy Neural Network Based on Fuzzy Clustering Algorithm;基于模糊聚类算法的自适应模糊神经网络研究
2.Application of Adaptive Fuzzy Artificial Neural Network to Observational Data Analysis of Dams自适应模糊神经网络在大坝安全监控中的应用
3.Prediction of Domestic Wool Top Properties Based on ANFIS基于自适应模糊神经网络的国毛毛条性能预测
4.On Sustainable Development Prediction and Adjustment Model Based on Adaptive Network-based Fuzzy Inference System;基于自适应模糊神经网络的可持续发展预测控制模型
5.Based on the Optimization of Adaptive Fuzzy Neural Network Control in Sewage Treatment基于参数优化的自适应模糊神经网络控制在污水处理中的应用
6.Research on identification of hydraulic turbine model based on adapting fuzzy neural networks基于自适应模糊神经网络的水轮机特性辨识研究
7.Short-Term Electricity Price Forecasting Based on Subtractive Clustering and Adaptive Neuro-Fuzzy Inference System;基于减法聚类及自适应模糊神经网络的短期电价预测
8.Study on Adaptive Control Based on Fuzzy-Neural Networks;基于模糊神经网络的自适应控制研究
9.A Self-Learning Multivariable Adaptive Controller Based on Fuzzy Logic Neural Network一种自学习模糊神经网络多变量自适应控制器
10.Research on Adaptive Neuron-Fuzzy Control for Self-balancing Two-wheeled Vehicle两轮自平衡小车自适应神经网络模糊控制研究
mon model adaptive control based on fuzzy neural network基于模糊神经网络的通用模型自适应控制
12.Adaptive Control for Switched Reluctance Motors Based on Fuzzy Neural Network;基于模糊神经网络的开关磁阻电机自适应控制
13.Adaptive Backstepping Fuzzy/Neural Networks Control of MIMO Nonlinear Systems;基于模糊/神经网络的非线性MIMO自适应反推控制
14.Research on Self-Adaptive Fuzzy Controller Based on Identification Using Neural Networks;基于神经网络辨识的自适应模糊控制器的研究
15.Adaptive group decision making method based on fuzzy distance and neural network;基于模糊距离和神经网络的自适应群决策方法
16.Fuzzy Adaptive Control Based on Neural Network for Shearer Lifting System采煤机调高系统的模糊神经网络自适应控制
17.A Tuning PI Current Control Approach of Inverter Based on Fuzzy Neural Network基于模糊神经网络的变换器自适应控制方法
18.Simulation of ANFIS Control for the Motion of Submarine in Vertical Plane潜艇垂直面运动自适应神经网络模糊控制仿真
相关短句/例句
ANFIS自适应神经网络模糊推理系统
1.Adequate datas used to train fuzzy rules and membership function in the BP neural network are obtained by means of LQR algorithm,thenANFIS controller is designed,in the end,a building is taken as the example to simulink under El Centro earthquake,the results shows thatANFIS is efficient in the structural active control.运用线性二次型经典最优控制算法获得学习样本,由神经网络反向传播算法训练产生模糊规则和隶属度函数,设计自适应神经网络模糊控制器,通过某结构地震波作用下振动控制的数字仿真,表明自适应神经网络模糊推理系统可以有效地应用到结构控制中。
2.Adaptive Neuro-Fuzzy Inference System(ANFIS) is a neural network system with one input and several outputs, which is got through achieving one step sugeno fuzzy system by the format of network.自适应神经网络模糊推理系统(AdaptiveNeuro FuzzyInferenceSystem, ANFIS)是将Sugeno一阶模糊系统以网络的形式来实现而得到的一种多输入单输出的神经网络。
3.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).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。
3)adaptive neural network-based fuzzy inference system自适应神经网络的模糊推理系统
1.Based on captured objective input-output data couple, a new ANFIS (adaptive neural network-based fuzzy inference system) framework, which can construct fuzzy membership function and fuzzy rules in Takagi-Sugeno fuzzy model, is used to model and control the complex inverted.该种方法采用BP算法与梯度下降法结合的混合算法对Takagi-Sugeno模糊模型中的前项及后件参数进行优化修正,在已获得的客观输入输出数据对的基础上,提出一种基于自适应神经网络的模糊推理系统ANFIS来对倒立摆系统进行建模和控制。
4)adaptive neural networks-fuzzy inference system自适应神经网络-模糊推理系统
1.A data fusion algorithm based onadaptive neural networks-fuzzy inference system(ANFIS) and D-S evidence theory is proposed.提出了一种基于自适应神经网络-模糊推理系统(ANFIS)和D-S证据理论相结合的信息融合方法。
5)adaptive fuzzy neural network自适应模糊神经网络
1.Studies on prediction of standard entropy of cations by usingadaptive fuzzy neural network;自适应模糊神经网络预测阳离子标准熵的研究
2.Studies on prediction of the stabilities of metal-HEDTA complex by usingadaptive fuzzy neural network;自适应模糊神经网络预测金属-HEDTA配合物稳定常数的研究
6)ANFIS自适应模糊神经网络
1.This system predicts the thickness error according to errors between predicted rolling forces and measured ones by applying adaptive networks based fuzzy inference system (ANFIS),and then adjusts the gaps of downstream stands dynamically.应用自适应模糊神经网络控制技术 ,建立了热带精轧机组动态设定系统 ,该系统根据前两个机架实测的轧制力和设定计算的轧制力间误差预测带钢可能出现的厚度偏差 ,根据模糊神经网络的预测结果修正后续机架的辊缝值 ,提高带钢头部厚度精度 。
2.This article utilizes the character of Gray Theory to predict the economy parameter,and then in order to obtain a better predicting effect,theANFIS is employed to predict its simulating error.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。
3.This article holds the purpose that,by usingANFIS,the eccentricity framework structure under the multi-dimensional earthquake excitation will be controlled,and the controlling force will be mount out through that madeANFIS being the controlling device and inputs the velocity and acceleration.目的应用自适应模糊神经网络对多维地震动下结构的振动进行主动控制。
延伸阅读
基于模糊神经网络的模具产品报价系统一、报价系统概论产品报价是指被讯价方根据自身所处市场环境、生产、经营、管理现状等因素而针对讯价方所指定的产品及其特殊的功能需求所报出的价格。产品报价是一种复杂而有重要的经济行为。产品报价的高低好坏有利于报价双方能面对面坐下来并经多次商讨而确定产品的成交价格并最终达成协议,签订合同。产品报价[1],特别是比较复杂的产品报价,如模具产品报价,需要许多领域人员的协调工作,如技术、财务、商务等,必须考虑各种结构化和非结构化的因素。其中结构化因素如技术参数、结构参数、工艺参数、制造成本、费用分配比例等比较易于确定的因素。而非结构化因素如最终利润率、赢得订单的几率等,则需要考虑企业内外环境等各种不确定因素。从信息系统角度来考虑,整个报价过程是一个信息流动和信息处理的过程,包括信息的产生、传递、处理、存储;具有很复杂的信息流,涉及到销售、经营、设计、会计、生产计划、采购等等。[1]目前国内外开发的报价系统依其功能可大致分为五类,即商务型报价系统、生产型报价系统、工程型报价系统、投标型报价系统和集成型报价系统。工程型报价系统实际上是产品选型、初步设计加成本估算,其最终报价的形成有待提高;商务型报价系统,是在技术报价的基础上,对产品价格进行分析、计算、结合价格变化趋势预测的结果,确定合适的产品价格。其全部价值是基于产品成本而做的加价判断或推理。二者各自突现了自己的重点,如前者对报价的结构化问题处理较好,而后者对报价所涉及的非结构化因素研究较为深刻。二、模具产品的报价模具产品的报价是一个非常复杂的过程。但从单纯的仅考虑结构化因素的技术报价来看。框一、功能分解与评价:根据客户提供的工件图纸及交货期限、或其他特殊的要求分析工件的结构特征、工艺参数等因素,提取有用信息。框二、产品方案设计:根据功能评价所提供的有用信息及交货期限等,考虑自身的生产、经营、管理现状,确定合理的方案。主要有工件排样、模具类型选择、压力机参数估算选型等。框三、结构设计:根据设计方案确定模具的合理结构和大致尺寸,同时选定模架形式等。框四、成本估算:根据工厂积累的有关经验数据(如外构件的价格、人工费用、材料费用、费用分配比例等)和以往开发同类产品的报价经验,由结构设计和方案设计所得的有关信息,估算产品成本。框五、历史经验资料、数据:为方案、结构、成本估算提供各种所需的资料、数据。包括各种工具书、国家标准、材料费用表、人工费用表、费用分配比例、以往开发经验及相关数据等非常有用的各种信息。