AI summary

This paper reviews basic backpropagation, presents the basic equations for back-propagation through time, and discusses applications to areas like pattern recognition involving dynamic systems, systems identification, and control. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives the theorem which underlies backpropagation is briefly discussed.

Abstract

Backpropagation is now the most widely used tool in the field of artificial neural networks. At the core of backpropagation is a method for calculating derivatives exactly and efficiently in any large system made up of elementary subsystems or calculations which are represented by known, differentiable functions; thus, backpropagation has many applications which do not involve neural networks as such. This paper first reviews basic backpropagation, a simple method which is now being widely used in areas like pattern recognition and fault diagnosis. Next, it presents the basic equations for back-propagation through time, and discusses applications to areas like pattern recognition involving dynamic systems, systems identification, and control. Finally, it describes further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations or true recurrent networks, and other practical issues which arise with this method. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives the theorem which underlies backpropagation-is briefly discussed.