We establish a relationship between general constrained
pseudoconvex optimization problems and globally projected dynamical
systems. A corresponding novel neural network model,
which is globally convergent and stable in the sense of Lyapunov,
is proposed. Both theoretical and numerical approaches are considered.
Numerical simulations for three constrained nonlinear optimization
problems are given to show that the numerical behaviors
are in good agreement with the theoretical results.