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!