What does the complete program structure of the Newff M in the Matalab Neural Network Toolbox look l

Differences between training functions and adaptive learning functions:

In terms of scope: the training function contains the learning function, and the learning function is a part of the training function;

In terms of error, the error of the training function is the smallest for the whole and the error of the learning function is the smallest for a single neuron.

halo, matrix problematic, p=[0; 1; 2; 3; 4; 5; 6; 7; 8; 9; 10;];

is the amount to correspond to the t-matrix.

An error message indicates that the number of dimensions you input to your network is incorrect.

Toolbox for Neural Networks in MATLAB: Type in NNtool, and a dialog box will pop up, and then you can follow the instructions of the pop-up box.

Using MATLAB 2010b and later versions, there will be a complete neural network toolbox, using nnstart to call up the toolbox, then select the required functions, import data, select training parameters and the number of neurons per layer, and finally the training will output the network and results.

The excitation function is used to calculate the output of the neuron from the input, while the training function and the learning function are based on the error, to modify the weights and thresholds, and then complete a training, and then continue to iterate, knowing that the number of iterations or the accuracy is satisfied.

However, the functions of the learning function and the training function seem to be very similar, and as for the specific differences, I am struggling with them.

The transfer function is well understood, it refers to the function of the output of each neuron to the input. The training function determines the weight and threshold adjustment of the entire neural network, while the learning function determines the adjustment of the weight and threshold after considering a single neuron, that is, the training function and the learning function are the relationship between the global adjustment threshold and the local adjustment threshold. More specifically, the training function uses the overall error to guide the learning function to train the local weights and thresholds.

Just give an example.

clear;

Enter the data matrix.

Landlord, first of all, I'm not a master.

Second, the 00 in the middle of your t is better written separately, and the 01 in threshold (if you want to separate).

Again, in newff, I changed it to this net=newff(threshold,[5,5],,traingdx'); Single quotation marks should be switched to English and typed in.

Finally, mine is ready to run.

You first enter doc newff in the command window, see how the help document calls newff, different versions of the toolbox call interface command format may be different, 2011b does not need theshold option, you better use direct input nnstart, call the neural network in the form of interface, it is more convenient to use, and you can also directly generate**.

What is your current version?

After r2016b, it can be compiled but there are limitations.

The parts that can't be compiled are those that contain simulink, and scripts that contain genfunction (to generate neural networks) and gensim (to generate independent simulink).

Previously, it seemed that only the trained model could be compiled.