A large number of iterations and oscillation are those of the major concern in solving the economic load dispatch problem using the Hopfield neural network. This paper develops two different methods, which are the slope adjustment and bias adjustment methods, in order to speed up the convergence of the Hopfield neural network system. Algorithms of economic load dispatch for piecewise quadratic cost functions using the Hopfield neural network have been developed for the two approaches. The results are compared with those of a numerical approach and the traditional Hopfield neural network approach. To guarantee and for faster convergence, adaptive learning rates are also developed by using energy functions and applied to the slope and bias adjustment methods. The results of the traditional, fixed learning rate, and adaptive learning rate methods are compared in economic load dispatch problems. ©1997 IEEE.
