Information Measures in the M-Hypercube

Information measures in the M-hypercube by entropy is presented in this paper and illustrated by an example. This method is completely general and applicable to M-hypercubes of any dimension with any number of inputs, state nodes and outputs. The evaluation of entropy measures has been introduced for the N-hypercube representation of combinational circuits by an algorithm known as induction of decision trees (ID3). The purpose of this paper is to present a general method for measuring information of signals in the M-hypercube which can be used to calculate the quantity of the random input, state, or output signals of the state machine represented by the hypercube.