selecting optimal portfolio with uncertain returns using a capable neural network model

This paper discusses the portfolio selection problem when security returns are uncertain variables and uses of a neural network based on a dynamic model to solve them. two types of portfolio selection programming models are provided based on uncertain theory and convert this models into crisp equivalent problem when the return rates are some special uncertain variables. is proved that in the proposed neural network the equilibrium point is equivalent to the optimal solution of the original problem and this nn model is stable and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. an illustrative example is provided to show the effectiveness of the proposed nn for this problem.