Adaptive networks using learning matrices

This paper describes, how to connect two LM's to form an “Autonomous Learning Matrix Dipole” (ALD) and how to organize it, so that it adapts itself to an environment according to a given evaluation scale.

Abstract

The performance of the Learning Matrix (LM) is suitable for the design of adaptive networks of higher complexity. It has been published, how to connect a LM with a generator of patterns (binary or nonbinary) and a ring-counter to result in an automatic classification of the presented patterns. This paper describes, how to connect two LM's to form an "Autonomous Learning Matrix Dipole" (ALD) and how to organize it, so that it adapts itself to an environment according to a given evaluation scale. For this purpose, a third type of input (beside "e" and "b"), namely "h" seems to be useful. This h-input controls the rate of adaptation of the LM. Using such ALD's, one may design adaptive structures of even higher complexity, for example with an adaptive internal model. The principle of "Learning Matrices" has been explained in detail (see e.g. IEEE Transactions on Electronic Computers, Vol. EC-12, No. 6, December, 1963, pp. 846–862). Using such "learning matrices" (LM), one may build up adaptive networks with rather interesting functions. Perhaps they are interesting for the physiologist and psychologist as well as for the engineer. Let us first recall the most essential details of the LM's.