A novel pipelined neural FIR architecture for nonlinear adaptive filter

Abstract

This paper presents a novel adaptive pipelined neural finite impulse response (PNFIR) filter for nonlinear signal processing. Unlike traditional pipelined recurrent neural network (PRNN), each module of the PNFIR filter is a simple architecture that includes a standard FIR filter followed by a nonlinear activation function. The complete design of proposed filter includes two subsections: The nonlinear part consists of neural FIR (NFIR) modules which is interconnected in a chained form and simultaneously executed in a parallel fashion; the linear subsection is a tapped-delay-line (TDL) linear combiner. Based on convex combination architecture, the adaptive algorithm derived from the gradient descent approach is utilized to update weights of the nonlinear and linear parts. Moreover, the analysis of stability conditions and computational complexity is also presented. Numerous simulation experimental results on nonlinear dynamic systems identification, speech signal and chaotic time series prediction show that the proposed PNFIR filter has simpler architecture, faster convergence rate, and lower computation complexity than the PRNN and joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV).