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This article mainly introduces the relevant knowledge of how to use the regression method of RBF network, the content is detailed and easy to understand, the operation is simple and fast, and has a certain reference value. I believe you will gain something after reading this RBF network regression method. Let's take a look at it.

% clear environment variables

Clc

Clear

% generate training samples (training input, training output)

Ld is the number of samples

Ld=400

% generate 2*ld matrix

X=rand (2jinld)

Convert x to [- 1.5 1.5]

X = (xMel 0.5) * 1.5cm 2

The first column of% x is x1 and the second column is x2.

X1fux (1dint:)

X2fux (2Jing:)

% calculate network output F value

Fair20 + x1 ^ 2-10*cos (2*pi*x1) + x2 ^ 2-10*cos (2*pi*x2)

Establish RBF neural network

Approximate RBF neural network was used. Spread is the default

Net=newrb (XBI F)

% create test sample

% generate the testing data

Interval=0.1

[I, j] = meshgrid (- 1.5:interval:1.5)

Row=size (I)

Tx1=i (:)

Tx1=tx1'

Tx2=j (:)

Tx2=tx2'

Tx= [tx1;tx2]

%% uses the established RBF network to simulate and get the network output

Ty=sim (net,tx)

%% use images to draw 3D pictures

Figure

% true function image

Interval=0.1

[x1, x2] = meshgrid (- 1.5:interval:1.5)

F = 20 + x1 ^ 2-10*cos (2*pi*x1) + x2 ^ 2-10*cos (2*pi*x2)

Subplot (1, 1, 3, 1)

Mesh (x1 ~ x2 ~ F)

Zlim ([0BL60])

Title ('real function image')

The function image obtained by% network

V=reshape (ty,row)

Subplot (1, 3, 2)

Mesh (iMagnejPercoll v)

Zlim ([0BL60])

Title ('RBF neural network results')

% error image

Subplot (1, 3, 3)

Mesh (x1 ~ x2 ~ Flyv)

Zlim ([0BL60])

Title ('error image')

Set (gcf,'position', [300,250900400])

This is the end of the article on "how to use the regression method of RBF network". Thank you for reading! I believe you all have a certain understanding of the knowledge of "how to use the regression method of RBF network". If you want to learn more knowledge, you are welcome to follow the industry information channel.

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