UNIK-OPT NN - NEURAL-NETWORK-BASED ADAPTIVE OPTIMAL CONTROLLER ON OPTIMIZATION MODELS/

When the future information for an optimization model is not complete, the model tends to incorporate such uncertainties as some assumptions on the coefficients. As time passes and more precise information is a ccumulated, the initial optimal solution may no longer be optimal, or even feasible. At this point, model builders want to modify the assume d and controllable coefficients to obtain the desired values of design ated decision variables. To aid this process, a neural network could e ffectively be applied. So we develop a tool UNIK-OPT/NN which can supp ort the construction and recall of the neural network model on top of the knowledge assisted optimization model formulator UNIK-OPT and the semantic neural network building aid UNIK-NEURO. By adopting a commonl y interpretable semantic representation of optimization and neural net work models, UNIK-OPT/NN can effectively automate most of the neural n etwork construction and recall procedure for optimal. control.