Convergent algorithms for pattern recognition in nonlinearly evolving nonstationary environment

Blaydon and Ho have recently proposed two algorithms to determine the probability p(A/x) that a sample with the set of attributes x belongs to a pattern class A, assuming a fixed p(A/x). The present letter modifies these algorithms to allow p(A/x) ≡ p
i
(A/x), i= 1, 2, ..., to evolve (not necessarily linearly) with i. Dynamic stochastic approximation arguments are used.