Fast adaptive filter (FAF) use in process instrumentation and automation systems

A vector-space interpretation of FAF (fast adaptive filter) algorithms is presented. These algorithms are shown to be different from the least-mean-square (LMS) algorithms or other stochastic gradient algorithms. The impulse response and convergence behavior of the FAF are faster than those of the adaptive noise canceller for the same input and initial condition. Thus the FAF is suitable for use in process detection and automation systems. Through realistic testing a structure based on FAF algorithms, intended to achieve additional reduction in computational requirements, was subjected to realistic testing. Improvements in numerical stability, tradeoffs in initialization, and fast convergence were achieved