The main contribution of this paper is using

optimal control theory for improving the convergence

rate of backpropagation algorithm. In the proposed

approach, the learning algorithm of backpropagation

is modeled as a minimum time control problem

in which the step-size of its learning factor is considered

as the input of this model. In contrast to the traditional

backpropagation, learning algorithms which

the step-size by trial and error, it is selected

adaptively based on optimal control criterion. The effectiveness

of the proposed algorithm is uated in

two simulations: XOR and 3-bit parity. In both simulation

examples, the proposed algorithm outperforms

well in speed and the ability to escape local minima.-The main contribution of this paper is using

optimal control theory for improving the convergence

rate of backpropagation algorithm. In the proposed

approach, the learning algorithm of backpropagation

is modeled as a minimum time control problem

in which the step-size of its learning factor is considered

as the input of this model. In contrast to the traditional

backpropagation, learning algorithms which

the step-size by trial and error, it is selected

adaptively based on optimal control criterion. The effectiveness

of the proposed algorithm is　uated in

two simulations: XOR and 3-bit parity. In both simulation

examples, the proposed algorithm outperforms

well in speed and the ability to escape　local minima.