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restem_12q asked . 2021-04-20

Differrence between feed forward & feed forward back propagation

I used neural netowrk MLP type to pridect solar irradiance, in my code i used fitnet() commands (feed forward)to creat a neural network.But some people use a newff() commands (feed forward back propagation) to creat their neural network. please what's difference between two types?? :
 
 
 net=fitnet(Nubmer of nodes in haidden layer); --> it's a feed forward ?? true??   

 net=newff(Nubmer of nodes in haidden layer); ---> it's a feed forward back propagation ??
Help me please wchich one can i choose for my case (prediction problem)???!!! Who appripriate??

deep learning , matlab , simulink ,

Expert Answer

Prashant Kumar answered . 2024-05-18 07:44:43

1. Regardless of how it is trained, the signals in a feedforward network flow in one direction: from input, through successive hidden layers, to the output.
 
2. Given a trained feedforward network, it is IMPOSSIBLE to tell how it was trained (e.g., genetic, backpropagation or trial and error)
 
3. A feedforward backpropagation net is a net that just happened to be trained with a backpropagation training algorithm. The backpropagation training algorithm subtracts the training output from the target (desired answer) to obtain the error signal. It then goes BACK to adjust the weights and biases in the input and hidden layers to reduce the error.
 
4. Current examples of feedforward nets are
 a. FITNET for regression and curve-fitting which calls the generic FEEDFORWARDNET
 b. PATTERNNET for classification and pattern-recognition which calls the generic FEEDFORWARDNET
 c. FEEDFORWARDNET

5. OBSOLETE examples of feedforward nets are

 

 a. NEWFIT for regression and curve-fitting which calls the generic NEWFF
 b. NEWPR for classification and pattern-recognition which calls the generic NEWFF 
 c. NEWFF
6. The default training functions for the above algorithms use backpropagation. However, the designer can train the nets any way they want.
 
7. The Neural Network Toolbox does not offer a genetic algorithm (GA). However, there are posts to the NEWSGROUP and ANSWERS regarding training with a GA
SEARCH WORDS NEWSGROUP HITS ANSWERS HITS
NEURAL GA 69 39
NEURAL GENETIC 132 56
 
8. None of the above are recommended for timeseries forecasting. The CURRENT timeseries forecasting algorithms are
 
 
 a. TIMEDELAYNET: Feedforward net with output that only depends on current and past inputs
 b. NARNET:       Feedback net with output that only depends on past outputs
 c. NARXNET:      Feedback net with output that depends on BOTH past outputs as well as current and past inputs

 


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