Can we use the recursive least squares as a learning algorithm to an ADALINE? I'm new to neural network, I study electrical engineering, and I just started working with ADALINEs.

I use Matlab, and in their Documentation they cite :


  However, here the LMS (least mean squares) learning rule, which is
  much more powerful than the perceptron learning rule, is used. The
  LMS, or Widrow-Hoff, learning rule minimizes the mean square error and
  thus moves the decision boundaries as far as it can from the training
  patterns.


The LMS algorithm is the default learning rule to linear neural network in Matlab, but a few days later I came across another algorithm which is : Recursive Least Squares (RLS) in a 2017 Research Article by Sachin Devassy and  Bhim Singh in the journal: IET Renewable Power Generation, under the title : Performance analysis of proportional resonant and ADALINE-based solar photovoltaic-integrated unified active power filter where they state :


  ADALINE-based approach is an efficient method for extracting
  fundamental component of load active current as no additional
  transformation and inverse transformations are required. The various
  adaptation algorithms include least mean square, recursive least
  squares etc.


My questions are:


Is RLS just like LMS (i mean can it be used as a learning algorithm too)? 
If yes, how can I customize my ADALINE to use RLS instead of LMS as a learning algorithm (preferably in Matlab, if not in Python) because I want to do a comparative study between the two Algorithm!
RLS is a second order optimizer, so, unlike LMS which takes into account an approximation of the derivative of the gradient, RLS also considers the second order derivative. You can study more about second order methods in sub-section &quot;8.6 Approximate Second-Order Methods&quot; of the following book available online:
https://www.deeplearningbook.org/contents/optimization.html
Deep Learning
An MIT Press book
Ian Goodfellow and Yoshua Bengio and Aaron Courville