The ordinary differential equation approach to convergence analysis described in Section 8.5 does not apply directly to the generalized Hebbian-learning algorithm (GHA). However, by expressing the synaptic-weight matrix W(n) in Eq. (8.91) as a vector made up of the individual columns of W(n), we may build on the asymptotic stability theory of the maximum eigenfilter. Hence, in light of what has been said here, explore the convergence behavior of the generalized Hebbian-learning algorithm.
