An auto-adaptive synthetic neural network for real-time separation of independent signal sources

Discusses the following classical signal processing problem: given N physically distinct measurements which represent a priori unknown linear combinations of N independent signal sources, the network autoadaptively extracts the original independent signals. The authors consider the N input/output case. The analysis is restricted to signals which are zero mean, stationary and either periodic or aperiodic, deterministic or nondeterministic and to a medium which is homogeneous and introduces no time delay. In practice, the source separation algorithm also works well for nonstationary signals. The authors investigated the constraints which the network´s learning rule must satisfy and tested the proposed learning rules with digital simulations. To achieve real-time operation, they implemented three different circuit designs for 2 and 6 input/output networks using 2 μm n-well analog VLSI hardware