A novel approach to the convergence of neural networks for signal processing

A novel deterministic approach to the convergence analysis of (stochastic) learning algorithms is presented. The link between the two is the new concept of time-average invariance, which is a property of deterministic signals but resembles that of stochastic signals which are ergodic and stationary. >