to the separation recovery of signals in this case the architecture is not introducing additional constraints on the network note that h in the forward path of the network may in general represent a matrix in the simplest case or a transfer function of a dynamic model furthermore in the event that m n h may be chosen to be the identity matrix the elements of the procedure and its advantages are now apparent further generalizations of the procedures for developing the architectures can also account for non minimum phase mixing environments these steps are straightforward application of the above procedure and hence will not be elaborated upon an important generalization is to include nonlinearity as part of the architecture u2014 explicitly one model is to include nonlinearity as a static mapping of the measurement variable m t in this event the adaptive network needs to include a compensating nonlinearity at its input stage thus the input must include an u201c inverse type u201d nonlinearity to counter act the measurement prior to further processing this type of mixing environment is encountered in wireless applications that include satellite platforms the dynamic architecture defined in this proper way ensures that a solution to the blind signal separation does exist we now move to the next step of defining the proper adaptive procedure algorithm which would enable the network to converge to one of its possible solutions consequently after convergence the network will retain the variable for signal processing recovery discrete state space representation and specialization to discrete time iir and fir filters the mutual information of a random vector y is a measure of dependence among its components and is defined as follows in the continuous case l ue8a0 y u222b y u2208 y ue89e p 1 ue8a0 y.