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This article mainly introduces "how to use the regression method of RBF network". In the daily operation, I believe that many people have doubts about how to use the regression method of RBF network. The editor consulted all kinds of data and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubts about "how to use the regression method of RBF network". Next, please follow the editor to study!

% clear environment variables

Clc

Clear

% generate input and output data

% set step size

Interval=0.01

% generate x1 x2

X1=-1.5:interval:1.5

X2=-1.5:interval:1.5

According to the function, the corresponding function value is obtained first as the output of the network.

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

%% Network Establishment and training

The input of% network establishment is [x1bot x2] and the output is F. Spread uses the default.

Net=newrbe ([x1x x2], F)

Effect verification of%% network

% We will bring back the original data to test the network effect:

Ty=sim (net, [x1bot x2])

We use images to see the fitting effect of the network on nonlinear functions.

Figure

Plot3 (x1 ~ x2 ~ F ~ r')

Hold on

Plot3 (x1 ~ x2 ~ tyj ~ bm.)

View (1136.36)

Title ('visual method to observe the fitting effect of accurate RBF neural network')

Xlabel ('x1')

Ylabel ('x2')

Zlabel ('F')

Grid on

At this point, the study on "how to use the regression method of RBF network" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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