Abstract: An improved arc furnace regulator employs neural circuits connected in a multi-layer network configuration with various weighted relationships between the successive layers which are automatically changed over time as a function of an error signal by means of the back-propagation method so that the regulator gradually improves its control algorithm as a result of accumulated experience. The network is implemented in software which can be developed and run on a PC with extra co-computing capability for greater execution speed. A second trainable neural network which emulates the arc furnace is used to develop the error signal, and is trained in mutually exclusive time periods with the training of the regular network.

Abstract: An improved arc furnace regulator employs neural circuits connected in a multi-layer network configuration with various weighted relationships between the successive layers which are automatically changed over time as a function of an error signal by means of the back-propagation method so that the regulator gradually improves its control algorithm as a result of accumulated experience. The network is implemented in software which can be developed and run on a PC with extra co-computing capability for greater execution speed. A second trainable neural network which emulates the arc furnace is used to develop the error signal, and is trained in mutually exclusive time periods with the training of the regular network.