  This paper proposes the deployment of the Hopfield's artificial neural
network (H-NN) approach to optimally assign power in optical code division
multiple access (OCDMA) systems. Figures of merit such as feasibility of
solutions and complexity are compared with the classical power allocation
methods found in the literature, such as Sequential Quadratic Programming (SQP)
and Augmented Lagrangian Method (ALM). The analyzed methods are used to solve
constrained nonlinear optimization problems in the context of resource
allocation for optical networks, specially to deal with the energy efficiency
(EE) in OCDMA networks. The promising performance-complexity tradeoff of the
modified H-NN is demonstrated through numerical results performed in comparison
with classic methods for general problems in nonlinear programming. The
evaluation is carried out considering challenging OCDMA networks in which
different levels of QoS were considered for large numbers of optical users.
