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This article mainly introduces "comparative analysis of GRNN and multilayer feedforward network BP examples". In daily operation, I believe many people have doubts about the comparative analysis of GRNN and multilayer feedforward network BP examples. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "comparative analysis of GRNN and multilayer feedforward network BP examples". Next, please follow the editor to study!

Both GRNN neural network and BP network can be used to predict freight volume, but for specific network training, GRNN needs to adjust fewer parameters, only one spread parameter, so it can predict the network faster and has greater computational advantages.

This is the implementation of the BP network:

Clear

Load best

% this is the relevant data saved by GRNN after prediction.

Nasty 13

P=desired_input

T=desired_output

Net_bp=newff (minmax (p), [nMagol 3], {'tansig','purelin'},' trainlm')

% training network

Net.trainParam.show=50

Net.trainParam.epochs=2000

Net.trainParam.goal=1e-3

% call TRAINLM algorithm to train BP network

Net_bp=train (net_bp,p,t)

Bp_prediction_result=sim (net_bp,p_test)

Bp_prediction_result=postmnmx (bp_prediction_result,mint,maxt)

Bp_error=t_test-bp_prediction_result'

Disp (['error of BP neural network traffic prediction is', num2str (abs (bp_error))]))

At this point, the study on "comparative analysis of GRNN and multi-layer feedforward network BP examples" 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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