Fast Non-Negative Least-Squares Learning in the Random Neural Network

Abstract

The random neural network is a biologically inspired neural model where neurons interact by probabilistically exchanging positive and negative unit-amplitude signals that has superior learning capabilities compared to other artificial neural networks. This paper considers non-negative least squares supervised learning in this context, and develops an approach that achieves fast execution and excellent learning capacity. This speedup is a result of significant enhancements in the solution of the non-negative least-squares problem which regard (a) the development of analytical expressions for the evaluation of the gradient and objective functions and (b) a novel limited-memory quasi-Newton solution algorithm. Simulation results in the context of optimizing the performance of a disaster management problem using supervised learning verify the efficiency of the approach, achieving two orders of magnitude execution speedup and improved solution quality compared to state-of-the-art algorithms.

Interpretable Modeling for Short- and Medium-Term Electricity Demand Forecasting

“…Several efficient algorithms to obtain the solution in Eq. 11 have been proposed in the literature [e.g., (Lawson and Hanson, 1995;Bro and De Jong, 1997;Timotheou, 2016)].…”

Interpretable modeling for short- and medium-term electricity load forecasting

“…As a result, the NNLS problem becomes a convex optimization problem. Several efficient algorithms to obtain the solution in ( 11) have been proposed in the literature (e.g., Lawson and Hanson, 1995;Bro and DeJong, 1997;Timotheou, 2016).…”