This paper presents a predictive coding model based on adaptive neuro-fuzzy inference system (ANFIS).
提出了一种利用神经模糊推理系统（ANFIS）构建预测器的图像压缩预测编码算法。
Prediction, modeling and optimization were also done using ANFIS and Fuzzy Multi-Objective optimization.
同时也运用ANFIS和模糊多目标优化进行预测、建模和优化。
The results indicate that the method is feasible and the prediction capability of ANFIS is superior to the RBF network.
结果表明，利用神经网络进行可靠度时序预测是可行的，并且ANFIS的预测能力要优于rbf。
The surface diagrams generated by ANFIS did not depict a clear relationship between the inputs and outputs as expected.
由ANFIS做出的表面图不能清晰的描绘出投入与产出之间的关系。
The results show that, ANFIS is a powerful tool for modelling human error risks, it not only improves the forecast accuracy, but saves time.
结果表明，ANFIS对于人因失误风险评价来说是一个强有力的工具，不仅提高了预测的精度而且节省了时间。
By training and checking of ANFIS, the results prove that the adaptive neuro-fuzzy controller has high precision and good effect of controlling.
通过对ANFIS的训练及检验，结果表明，该自适应神经模糊控制器具有较高的控制精度，控制效果较好。
By training and checking of ANFIS, the results prove that the adaptive neuro-fuzzy controller owns high precision and good effect of controlling.
对ANFIS训练及检验的结果表明，该自适应神经模糊控制器具有较高的控制精度，控制效果较好。
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.
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。
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.
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。
According to linear request of the entire bridge main arch rib, use fuzzy theory and the ANFIS inference system forecast that various stages raise in advance.
根据全桥主拱肋线形要求，利用模糊理论及ANFIS推理系统预测各节段的预抬高值。
Therefore, the ANFIS model provides predictions with high accuracy, which proves to be a new approach for estimation of permeability and inertial coefficient.
所建立的预测渗透率和惯性系数的ANFIS模型，能够给出具有足够精度的预测结果，这为渗透率和惯性系数的预测开辟新的途径。
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.
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。
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).
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。
Compared with some other modeling methods, such as ANFIS, the proposed model is of less computation, higher accuracy, especially for high dimension data modeling.
与其他建模方法相比，如anfis，模糊树模型计算量小，精度高，尤其在高维数据建模中更为明显。
Absrtact: Application of adaptive noise cancellation with ANFIS based on GA is presented, explains its main idea and the implementation procedure of the algorithm.
摘要：介绍了应用基于GA的ANFIS的自适应噪声消除的方法，阐述了基本思想和算法实现过程。
The ANFIS design method is a blend intelligent system which combines the Fuzzy Logic system (FLS) and the Annual Neural Network (ANN) and USES their's strongpoints.
ANFIS设计方法是一种将模糊逻辑系统(FLS)和人工神经网络系统(ann)相结合，利用两者各自的优点所形成的混合智能系统。
The structure of ANFIS is proposed. Then a mixed learning arithmetic based on back promulgate and least-square arithmetic is presented to modify the network parameters.
给出了该自适应网络的结构，在此基础上给出了网络权值的修正算法，即综合最陡下降法和最小二乘法得到的一种混合学习算法。
Applying Adaptive Neural-Fuzzy Inference System (ANFIS) can produce fuzzy rules and adjust membership functions automatically based on data without experience of experts.
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。
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。
Adaptive neural fuzzy inference system (ANFIS), as a local approximation approach, could be used to model the quantitative structure-activity relationship (QSAR) of medicine.
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。
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.
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。
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.
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。
This new algorithm greatly raises the speed of parameter identification and computation convergence. An avionic equipment cost estimation model is set up based on ANFIS network.
混合学习算法提高了网络参数的辨识速度和网络计算的收敛速度。
The simulation results show that both methods are more powerful than ANFIS method in modeling complicate multi-input systems concerning with efficiency and precision of the model.
具体的仿真试验表明这两种建模方法对于复杂的多输入系统的建模，在效率与精度上比anfis模型及其他模型都要好。
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。
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.
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。
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。
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.
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。
The subsidence of the indoor model test is also predicted with this theory. The observed data are compared with the predicted data with the adaptive neuro-fuzzy inference system (ANFIS).
对室内模型试验进行沉降预测，并和实验观测数据以及自适应神经网络系统(ANFIS)预测结果进行了比较。
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。
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