Neeta Dsouza answered . 2024-05-18 08:55:51

 x = [250:1]

 x =  Empty matrix: 1-by-0

 1. Unfortunately, the standard terminology for this type of NN 
    is two-layer Multilayer Perceptron. 
     a. It corresponds to the two weight-layers or, equivalently, the 
     two neuron(i.e., activation-function)-layers.
     b. In particular, it is called a two-layer network even though 
     there are three layers of nodes: input, hidden, and output.
     c. Since the signals are represented by the nodes, I would 
     prefer to call it a three (node) layer net. So, to avoid confusion, 
     I simply refer to the net as a single hidden layer net.

 2.The 1st box represents an input fan-in unit (node layer) with as 
    many input nodes as the dimensionality of the input vector.
  3. The hidden neuron layer signal is represented by the output 
    of the 2nd box  
 4.   The output neuron layer signal is represented by the output 
    of the 3rd box.
  5. The existence of the 4th box is misleading. So, just imagine it 
    does not exist and label the arrow coming from the 3rd box as the 
    output signal.
   6. The weight and bias subboxes in the hidden layer box are 
    usually labeled IW and b1. 
   7. The weight and bias subboxes in the output layer box are 
    usually labelled LW and b2.
    8. My personal preference for diagramming would be to replace 
    the 1st and 4th boxes with arrows labelled x(input) and y(output).
    The arrow between the 2nd and 3rd boxes would be labelled 
    h(hidden).

 [ 10 1 ] = size(IW)% IW connects the 1-D input and the 10-D hidden layer signal
 [ 10 1 ] = size(b1) % b1 connects the 1-D input bias and the 10-D hidden layer signal

 h = tansig( b1 + IW*x); % [ 10 1 ] = size(h)

 [ 1 10 ] = size(LW)% LW connects and the 10-D hidden layer signal and the the 1-D output 
 [ 1 1 ] = size(b2) % b2 connects the 1-D output bias and the 1-D output layer signal

 y = purelin( b2 + LW*h); % [ 1 1 ] = size(y); purelin(z) = z

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