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此源代码是C++开发的指纹识别程序,对指纹的采取进行了相应的处理功能-This source code is a C development of fingerprint identification process to take fingerprints for the corresponding processing function
Update : 2024-05-05 Size : 29696 Publisher : 常俊芳

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模糊模式识别,包括最小最大贴近度、最小平均贴近度、海明贴近度、欧几里德贴近度等-Fuzzy pattern recognition, including the largest close to the smallest degree, the smallest average close degree, Haiming close degree, close to the degree of Euclidean
Update : 2024-05-05 Size : 2048 Publisher : 李明

robust control toolbook
Update : 2024-05-05 Size : 855040 Publisher : 徐达

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THIS MATLAB CODE REDUCE THE SPECKLE NOISE IN SAR IMAGE, IT USE WAVELET FILTER THEN USED CASCADE THREE FILTERS IN TIME DOMAIN (HYBRID TIME AND FREQUENCY DOMAIN). THIS CODE NEED SOME MODIFICATIONS SINCE THERE ARE SOME PROBLEMS LIKE BY COMPUTE PSNR AND THE WAVELET HAS SOME ERRORS IN COMPUTE THE DE-NOISING
Update : 2024-05-05 Size : 1214464 Publisher : same

Nonlinear System Identification This demo addresses the use of ANFIS function in the Fuzzy Logic Toolbox(TM) for nonlinear dynamical system identification. This demo also requires the System Identification Toolbox(TM), as a comparison is made between a nonlinear ANFIS and a linear ARX model. Copyright 1994-2007 The MathWorks, Inc. $Revision: 1.9.2.4 $- Nonlinear System Identification This demo addresses the use of ANFIS function in the Fuzzy Logic Toolbox(TM) for nonlinear dynamical system identification. This demo also requires the System Identification Toolbox(TM), as a comparison is made between a nonlinear ANFIS and a linear ARX model. Copyright 1994-2007 The MathWorks, Inc. $Revision: 1.9.2.4 $
Update : 2024-05-05 Size : 17408 Publisher : Mohammed

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This demo addresses the use of ANFIS function in the Fuzzy Logic Toolbox(TM) for nonlinear dynamical system identification. This demo also requires the System Identification Toolbox(TM), as a - This demo addresses the use of ANFIS function in the Fuzzy Logic Toolbox(TM) for nonlinear dynamical system identification. This demo also requires the System Identification Toolbox(TM), as a
Update : 2024-05-05 Size : 5120 Publisher : Mohammed

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本文研究了模糊系统和神经网络这两种人工智能方法的长处和短处,并将它们有机地结合在一起。将基于T-S模型的模糊神经网络应用于水质评价中,取得了较好的效果,为水质评价提供了一个新的方法。 -In this paper, fuzzy systems and neural network artificial intelligence methods both strengths and weaknesses, and they are organically combined. TS model based fuzzy neural network used in water quality assessment, and achieved good results for water quality assessment provides a new approach.
Update : 2024-05-05 Size : 3101696 Publisher : 陈宇航

if a radio configuration is given, what are its anticipated capabilities (e.g., in terms of achievable data rate), taking into account recent information sensed, as well as the past experience and knowledge?”.Here learning schemes are considered for basic and extended based on neural networks and ANFIS are designed to enhance the learning capabilities of a cognitive terminal, in terms of assisting it to predict the data rate that a specific radio configuration could achieve if it was selected for operation.
Update : 2024-05-05 Size : 2048 Publisher : Madhusmita

Here present ed working of the fifth generation intelligent radio that is Cognitive Radio (CR) system which works on predictive data rate and ANFIS based learning scheme is proposed to introduce intelligence in it. The performance of this has been compared with previous neural network based schemes.-Here present ed working of the fifth generation intelligent radio that is Cognitive Radio (CR) system which works on predictive data rate and ANFIS based learning scheme is proposed to introduce intelligence in it. The performance of this has been compared with previous neural network based schemes.
Update : 2024-05-05 Size : 2048 Publisher : Madhusmita

working of the fifth generation intelligent radio that is Cognitive Radio (CR) system which works on predictive data rate and propose ANFIS based learning scheme to introduce intelligence in it. The testing data set is taken from training set. Validation data set is unseen data. RMSE and prediction accuracy are used as performance index. Two gaussian membership functions are taken for each input. Error goal was set for .001.Training was for 300 epochs. It is seen prediction accuracy was 91 percentages in testing case and 89 in validation. RMSE difference between validation and testing case very small. - working of the fifth generation intelligent radio that is Cognitive Radio (CR) system which works on predictive data rate and propose ANFIS based learning scheme to introduce intelligence in it. The testing data set is taken from training set. Validation data set is unseen data. RMSE and prediction accuracy are used as performance index. Two gaussian membership functions are taken for each input. Error goal was set for .001.Training was for 300 epochs. It is seen prediction accuracy was 91 percentages in testing case and 89 in validation. RMSE difference between validation and testing case very small.
Update : 2024-05-05 Size : 2048 Publisher : Madhusmita

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Adaptive Neuro-Fuzzy Inference System
Update : 2024-05-05 Size : 1491968 Publisher : Amir

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AnFis matlab code, simulates ANFIS.
Update : 2024-05-05 Size : 1024 Publisher : Amir

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ANFIS sample code, simulation in matlab.
Update : 2024-05-05 Size : 2048 Publisher : Amir

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farsi document that describes ANFIS.
Update : 2024-05-05 Size : 229376 Publisher : Amir

这个是我看文献做的一个anfis(自适应神经网络模糊推理系统)的一个例子,已经表明注释,很容易上手-This is a code for anfis(Adaptive network-based fuzzy inference system),when you looke ie you can got it!
Update : 2024-05-05 Size : 1491968 Publisher : 吴遇桑

matlabenso
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weather prediction using ANFIS
Update : 2024-05-05 Size : 1024 Publisher : chandu

channel equalisation using anfis
Update : 2024-05-05 Size : 5120 Publisher : manoranjan rajguru

本文提出一种新的智能故障诊断方法,基于统计分析,提出了一种改进的距离的评价技术和适应性类神经模糊推论系统(简称ANFIS)。该方法包括三个阶段。 -This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy
Update : 2024-05-05 Size : 176128 Publisher : 张力

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Simulation for neural networks in matlab
Update : 2024-05-05 Size : 1233920 Publisher : sona

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ANFIS: Adaptive Neuro-Fuzzy Inference Systems
Update : 2024-05-05 Size : 1024 Publisher : melek
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