Qualitative Behavior of Differential Equations Associated with Artificial Neural Networks

We establish conditions under which Oja–Adams' learning models are gradient, semi-gradient, or gradient-like systems. We consider both single and multi-output models. The multi-output learning models are represented by matrix differential equations whose analysis require techniques from matrix calculus. We also derive the stability behavior of these systems while restricted to naturally defined invariant sets.