Solving Time of Least Square Systems in Sigma-Pi Unit Networks

The solving of least square systems is a useful operation in neurocomputational modeling of learning, pattern matching, and pattern recognition. In these last two cases, the solution must be obtained on-line, thus the time required to solve a system in a plausible neural architecture is critical. This paper presents a recurrent network of Sigma-Pi neurons, whose solving time increases at most like the logarithm of the system size, and of its condition number, which provides plausible computation times for biological systems. Keywords- Least Square Systems, On-line Pattern Matching, RBFN Learning, Sigma-Pi Neurons, Recurrent Neural Network.

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Pierre Courrieu
• Keywords: CORR 2008, Education, Pattern Matching, Sigma-pi Neurons, Square Systems