BP-Stoc. Appr.

[William Finnoff]

  I'm looking for additional references on the relationship between pattern
for pattern backpropagation and stochastic
approximation/algorithms/optimization (Robins Munroe, etc.).  I am aware of
the following

 White, H., Some asymptotic results for learning in single hidden-layer
feedforward network models, Jour. Amer. Stat. Ass. 84, no. 408, p.
1003-1013, (1989),
       
 White, H., Learning in artificial neural networks: A statistical
perspective, Neural Computation 1, 1989, pp. 425-464,

 Darken C. and Moody J., Note on learning rate schedules for stochastic
optimization, in Advances in Neural Information Processing Systems 3.,
Lippmann, R. Moody, J., and Touretzky, D., ed., Morgan Kaufmann, San Mateo,
(1991),

and the latest contribution of the last two authors at NIPS(91).  I have a
fairly good list of literature with regards to the general theory of
stochastic algorithms.  Three examples are listed below:

Bouton C., Approximation Gaussienne d'algorithmes stochastiques a dynamique
Markovienne.  Thesis, Paris VI, (in French), (1985)

Kushner, H.J., and Schwartz, A., An invariant measure approach to the
convergence of stochastic approximations with state dependent noise. SIAM
j. Control and Opt. 22, 1, p. 13-27, (1984)

Metivier, M. and Priouret, P., Th'eor`emes de convergence presque-sure pour
une classe d'algorithmes stochastiques `a pas d'ecroissant. Prob. Th. and
Rel. Fields 74, p. 403-28, (in French), (1987).

 
I am therefore only interested in references in which the relationship to
BP is explicit.  Any help in this matter will be appreciated.