Fuzzy-Neural-Network-by-matlab

代码说明：

  这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。(This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy network trained exclusively by error backpropagation at layers 2 and 4. The network employs 4 sets of adjustable parameters. In Layer 2: mean[i,j], sigma[i,j] and Theta[i,j] and in Layer 4: Weights w4[m,j]. The network uses considerably less adjustable parameters than ANFIS/CANFIS and therefore, its training is generally faster. This makes it ideal for on-line learning/operation. Also, its approximating/mapping power is increased due to the employment of dynamic elements within Layer 2. Scatter-type and Grid-type methods are selected for input space partitioning.)

文件列表：

Demos
.....\rfnn_mimo_grid_demo.mdl,55483,2013-08-10
.....\rfnn_mimo_scatter_demo.mdl,55355,2013-08-10
.....\rfnn_miso_grid_demo.mdl,51971,2013-08-10
.....\rfnn_miso_grid_demo2.mdl,44956,2013-08-10
.....\rfnn_miso_grid_demo3.mdl,37496,2013-08-10
.....\rfnn_miso_scatter_demo.mdl,44893,2013-08-10
.....\rfnn_miso_scatter_demo2.mdl,37469,2013-08-10
.....\Utilities
.....\.........\MG_Check.dat,113147,2011-03-15
.....\.........\MG_Train.dat,113147,2011-03-15
.....\.........\SimDataGenAnfis1.m,593,2013-08-10
Library
.......\RFNN_matlab.mdl,45831,2013-08-10
license.txt,1315,2013-09-24
S-functions
...........\comb.m,1148,2013-08-10
...........\rfnn_mimo_grid.m,6373,2013-08-04
...........\rfnn_mimo_scatter.m,6211,2013-08-04
...........\rfnn_miso_grid.m,6199,2013-08-04
...........\rfnn_miso_scatter.m,6098,2013-08-04