搜索资源列表
anfis
- 用模糊神经网络逼近二维非线性函数，Matlab文件，附有说明文件。
LAGI
- MATLAB code for ANFIS methode,(identification and validation)
Anfis
- Fuzzy control Design using ANFIS for a power system with svc
anfisprogramme
- 讲述了ANFIS构建的Matlab程序，具有一定的实用性和理论意义。-Tells Matlab built ANFIS procedure has certain practical and theoretical significance.
dryd
- This addresses the use of ANFIS function in the Fuzzy Logic Toolbox for nonlinear dynamical system identification. This also requires the System Identification Toolbox, as a comparison is made between a nonlinear ANFIS and a linear ARX model.
WAVELET_WORK
- 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
anfis
- anfis tutorial and some basic in use in matlab.
netrual_net
- AnFis matlab code, simulates ANFIS.
neurofuzzy-anfis
- neuro anfis with Matlab
ANFIS
- Its a MATLAB program to predict a series of data.
Evolutionary-ANFIS-Training
- 用MATLAB实现自适应神经模糊推理系统（ANFIS）结构训练。代码中，首先创建一个初始原ANFIS结构，然后采用遗传算法（GA）、粒子群优化（PSO）来训练ANFIS。此进化训练算法可用于解决非线性回归函数逼近问题。-Implementation of adaptive neural fuzzy inference system (ANFIS) based on MATLAB. Code, the first to create an initial original ANFIS struct
anfis(command-program)
- It is Adoptive Neuro Fuzzy(ANFIS) program written in Matlab Software.
AR
- program anfis for matlab wireless
anfis
- fuzzy anfis cource code programming
模糊神经网络逼近二维非线性函数
- 采用模糊神经网络逼近二维非线性函数的matlab实例，matlab2008以上可以运行(Fuzzy neural network is used to approximate two dimensional nonlinear function of MATLAB instance, and matlab2008 can be used to run)
ANFIS
- ANFIS Algorithm in Matlab
ANFIS algorithm
- 自适应神经模糊推理系统的可运行实例，注释清楚易懂(Operable examples of adaptive neuro-fuzzy inference system)
ANFIS Nonlinear Regression
- ANFIS Nonlinear Regression in Matlab
1.1 ANFIS Neural Network
- ANFIS Neural Network
Model3_ANFIS
- 基于matlab的anfis模型，效果很好，但是仅适用于特征较少的数据集(Anfis model based on matlab, which works well, but only for data sets with fewer features)