Question on "newlind" from Neural Network Toolbox

Dear community,

I would like to know what algorithm exactly (LMS ?) describes the matlab estimation function newlind. http://www.mathworks.de/de/help/nnet/ref/newlind.html

The documentation of Matlab does not offer full disclosure on the function, but only what it does. More info very appreciated.

Kind regards

Answer 1

[Greg Heath]

Use type, doc or help to get the answer. For example,

 type newlind

 function out1 = newlind(varargin)
%NEWLIND Design a linear layer.
%
%  newlind(X,T) takes an RxQ input matrix X, an SxQ target matrix T, and
%  returns a linearlayer designed to output T (with minimum sum square
%  error) given X.
%
%  newlind(X,T,Xi) can also solve for linear networks with input delays and
%  multiple inputs and layers by supplying input and target time series
%  data in cell array form:
%    X  - NixTS cell array, each element X{i,ts} is an RixQ input matrix.
%    T  - NtxTS cell array, each element T{i,ts} is an VixQ matrix.
%    Xi - NixID cell array, each element Xi{i,k} is an RixQ matrix, default = [].
%  returns a linear network with ID input delays, Ni network inputs, Nl layers,
%  and  designed to output T (with minimum sum square error) given input P.

Now try the doc and help commands.

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[Greg Heath]

Use the type command:

 type newlind

Hope this helps

Thank you for formally accepting my answer

Greg

Answer 3

[Erol H]

Thanks, but i would like to know if the newlind command uses the LMS (Least mean squares) algorithm or not ?

Answer 4

[Star Strider]

In the newlind documentation under More About > Algorithms:

• newlind calculates weight W and bias B values for a linear layer from inputs P and targets T by solving this linear equation in the least squares sense:

     [W b] * [P; ones] = T

So the answer to your question is: Yes. ‘Least squares’ means ‘least sum of squares’ that is essentially least mean squares. (It doesn’t take the mean but the result is essentially the same.)