Past Events

Optimization problems arise in a wide variety of scientific and engineering applications. It is
computationally challenging when optimization procedures have to be performed in real time to optimize
the performance of dynamical systems. For such applications, classical optimization techniques may not be
competent due to the problem dimensionality and stringent requirement on computational time. One very
promising approach to dynamic optimization is to apply artificial neural networks. Because of the inherent
nature of parallel and distributed information processing in neural networks, the convergence rate of the
solution process is not decreasing as the size of the problem increases. Neural networks can be implemented
physically in designated hardware such as ASICs where optimization is carried out in a truly parallel and
distributed manner. This feature is particularly desirable for dynamic optimization in decentralized
decision-making situations. In this talk, we will present the historic review and the state of the art of
neurodynamic optimization models and selected applications. Specifically, starting from the motivation of
neurodynamic optimization, we will review various recurrent neural network models for optimization.
Theoretical results about the stability and optimality of the neurodynamic optimization models will be
given along with illustrative examples and simulation results. It will be shown that many computational
problems, such as k winner-take-all, can be readily solved by using the neurodynamic optimization models.