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In this article, the editor introduces in detail "matlab gasoline octane number prediction case analysis based on near infrared spectrum", the content is detailed, the steps are clear, and the details are handled properly. I hope this article "matlab gasoline octane number prediction case analysis based on near infrared spectrum" can help you solve your doubts.

This paper compares the application of two widely used supervised learning neural networks-BP neural network and RBF neural network in regression fitting.

% clear environment variables

Clear

Clc

%% training set / test set generation

Load spectra_data.mat

% randomly generate training set and test set

Temp = randperm (size (NIR,1))

% training set-50 samples

P_train = NIR (temp (1:50),:)'

T_train = octane (temp (1:50),:)'

% Test set-10 samples

P_test = NIR (temp (51:end),:)'

T_test = octane (temp (51:end),:)'

N = size (Pruntestline 2)

%% BP neural network creation, training and simulation testing

% create a network

Net = newff (Prune Magnum thumbnail Magna 9)

% set training parameters

Net.trainParam.epochs = 1000

Net.trainParam.goal = 1e-3

Net.trainParam.lr = 0.01,

% training network

Net = train (net,P_train,T_train)

% Simulation Test

T_sim_bp = sim (net,P_test)

% RBF neural network creation and simulation test

% create a network

Net = newrbe (packs, girls, girls and girls)

% Simulation Test

T_sim_rbf = sim (net,P_test)

%% performance evaluation

% relative error error

Error_bp = abs (T_sim_bp-T_test). / T_test

Error_rbf = abs (T_sim_rbf-T_test). / T_test

% determination coefficient R ^ 2

R2_bp = (N * sum (T_sim_bp. * T_test)-sum (T_sim_bp) * sum (T_test)) ^ 2 / ((N * sum ((T_sim_bp). ^ 2)-(sum (T_sim_bp)) ^ 2) * (N * sum ((T_test). ^ 2)-(sum (T_test)) ^ 2))

R2_rbf = (N * sum (T_sim_rbf. * T_test)-sum (T_sim_rbf) * sum (T_test)) ^ 2 / ((N * sum ((T_sim_rbf). ^ 2)-(sum (T_sim_rbf)) ^ 2) * (N * sum ((T_test). ^ 2)-(sum (T_test)) ^ 2))

% result comparison

Result_bp = [Troutest'This simplex bp'This simplex rbf' error_bp' error_rbf']

%% drawing

Figure

Plot (1RH NMagneThemagrbfjngkLizi.)

Legend ('real value','BP predicted value', 'RBF predicted value')

Xlabel ('forecast sample')

Ylabel ('octane number')

String = {'comparison of prediction results of octane number content in test set (BP vs RBF)'; ['R ^ 2 = 'num2str (R2_bp)' (BP)''R ^ 2 = 'num2str (R2_rbf)' (RBF)']}

Title (string)

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