In digital filter theory, the identification process describes internal dynamic states based on a reference system, commonly known as a black box. The identification process as a function of: a) transition function, b) identified delayed states, c) gain function which depends on convergence correlation error, and d) an innovation process based on the error described by the differences between the output reference system and the identification result. Unfortunately, in the black box concept, the exponential transition function considers the unknown internal parameters. This means that the identification process does not operate correctly because its transition function has no access to the internal dynamic gain. An approximation for solving this problem includes the estimation in the identification technique. This paper presents an estimation for a "single input single output" (SISO) system with stationary properties applied to internal state identification.

In digital filter theory, the identification process describes internal dynamic states based on a reference system, commonly known as a black box. The identification process as a function of: a) transition function, b) identified delayed states, c) gain function which depends on convergence correlation error, and d) an innovation process based on the error described by the differences between the output reference system and the identification result. Unfortunately, in the black box concept, the exponential transition function considers the unknown internal parameters. This means that the identification process does not operate correctly because its transition function has no access to the internal dynamic gain. An approximation for solving this problem includes the estimation in the identification technique. This paper presents an estimation for a "single input single output" (SISO) system with stationary properties applied to internal state identification.