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This article Xiaobian for you to introduce in detail "what is the method of nonlinear function fitting of matlab BP neural network", the content is detailed, the steps are clear, and the details are handled properly. I hope that this article "what is the method of nonlinear function fitting of matlab BP neural network" can help you solve your doubts.

Some complex nonlinear systems are often encountered in engineering applications, and the state equations of these systems are complex, so it is difficult to model accurately by mathematical methods. In this case, a BP neural network can be established to express these nonlinear systems. In this method, the unknown system is regarded as a black box. Firstly, the BP neural network is trained with the system input and output data, so that the network can express the unknown function, and then the trained BP neural network can be used to predict the system output.

% clear environment variables

Clc

Clear

%% training data prediction data extraction and normalization

Download input and output data

Load data input output

% randomly sorted from 1 to 2000

K=rand (1JI 2000)

[mmaine n] = sort (k)

% find training data and forecast data

Input_train=input (n (1 1900),:)'

Output_train=output (n (1 1900))

Input_test=input (n (1901 2000),:)'

Output_test=output (n (1901 2000))

% selected sample input and output data normalization

[inputn,inputps] = mapminmax (input_train)

[outputn,outputps] = mapminmax (output_train)

%% BP network training

Initialize network structure

Net=newff (inputn,outputn,5)

Net.trainParam.epochs=100

Net.trainParam.lr=0.1

Net.trainParam.goal=0.00004

% Network training

Net=train (net,inputn,outputn)

% BP Network Forecast

% prediction data normalization

Inputn_test=mapminmax ('apply',input_test,inputps)

% network forecast output

An=sim (net,inputn_test)

% network output is de-normalized

BPoutput=mapminmax ('reverse',an,outputps)

%% result analysis

Figure (1)

Plot (BPoutput,':og')

Hold on

Plot (output_test,'-*')

Legend ('predicted output', 'expected output')

Title ('BP network prediction output', 'fontsize',12)

Ylabel ('function output', 'fontsize',12)

Xlabel ('sample', 'fontsize',12)

% prediction error

Error=BPoutput-output_test

Figure (2)

Plot (error,'-*')

Title ('BP network prediction error', 'fontsize',12)

Ylabel ('error', 'fontsize',12)

Xlabel ('sample', 'fontsize',12)

Figure (3)

Plot ((output_test-BPoutput). / BPoutput,'-*')

Title ('neural network prediction error percentage')

Errorsum=sum (abs (error))

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