Abstract
A novel data‐driven learning approach of nonlinear system represented by neural fuzzy Hammerstein‐Wiener model is presented. The Hammerstein‐Wiener system has two static nonlinear blocks represented by two independent neural fuzzy models surrounding a dynamic linear block described by finite impulse response model. The multisignal theory is designed for employing Hammerstein‐Wiener system to separate parameter learning issues. To begin with, the output nonlinearity parameters are learned utilizing separable signal with different amplitudes. Furthermore, correlation analysis method is implemented for estimating linear block parameters using separable signal inputs and outputs; thereby, the interference of process noise is effectively handled. Finally, multi‐innovation learning technology is introduced to improve system learning accuracy, and then, multi‐innovation extended stochastic gradient algorithm is obtained for optimizing input nonlinearity and noise model using multi‐innovation technique and gradient search method. The simulation results display that presented data‐driven learning approach has the availability of learning Hammerstein‐Wiener system.
Highlights
The real industrial processes are almost nonlinear systems to some extent, and linear approximation means are usually unacceptable, and nonlinear models should be taken into account that they can present the nonlinearity successfully
Block-oriented nonlinear models which are composed of linear dynamic block and static nonlinear functions for instance Hammerstein model and Wiener model have been performed on account of their simple structures
This paper focuses attention on a three-stage parameter learning approach of the Hammerstein-Wiener nonlinear systems with stochastic disturbances using multisignal data
Summary
The real industrial processes are almost nonlinear systems to some extent, and linear approximation means are usually unacceptable, and nonlinear models should be taken into account that they can present the nonlinearity successfully. Many contributions in existing literatures have developed to learn nonlinear system represented by Hammerstein-Wiener model, the problem of stochastic disturbances is not fully considered. This paper focuses attention on a three-stage parameter learning approach of the Hammerstein-Wiener nonlinear systems with stochastic disturbances using multisignal data. In order to achieve a fast convergence rate of stochastic gradient algorithm, multiinnovation-based extended stochastic gradient scheme by expanding the scalar innovation to an innovation vector is used to learn parameters of input nonlinearity and noise model. (1) Multisignal theory is designed to employ the Hammerstein-Wiener system to separate parameter learning issues, thereby avoiding redundant parameters (2) The unmeasurable problems of HammersteinWiener system are well settled by using correlation analysis method (3) The multi-innovation-based extended stochastic gradient scheme by expanding the scalar innovation to an innovation vector is used to achieve a fast convergence rate.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
