On the Learning Behavior of Stochastic Automata Under a Nonstationary Random Environment

We propose a new nonstationary random environment R(C
₁
(t,ω),...,C
_r
(t,ω)), where t represents time and ω ∈ Ω, Ω being the supporting set of a probability measure space (Ω,B,μ). Moreover, the learning performance of the L
_r-1
scheme under R(C
₁
(t,ω),..., C
_r
(t,ω)) is discussed.