Identification of Distributed Systems with Identical Subsystems

Abstract: This article presents an identification technique for distributed systems with identical units using linear recurrent neural networks and exploiting the replicated structure of the units inside the system. The proposed method is applicable both to open-loop and closed-loop identification, takes into consideration boundary conditions and available information about the structure of the system, and is capable of identifying systems with heterogeneous units. The approach provides parameters estimate with minimum bias for unstable plant models when there is additive colored noise in the data. The method is described for two-dimensional systems (one for time and one for space), but is equally applicable to systems having more dimensions in space. The effectiveness of the method is demonstrated by two examples. 1