Identification method of neuro‐fuzzy‐based Hammerstein model with coloured noise

Li, Feng; Jia, Li; Peng, Daogang

doi:10.1049/iet-cta.2017.0306

Cited by 39 publications

(21 citation statements)

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“…The tasks of parameter learning method are to estimate Hammerstein-Wiener system parameters, that is, two nonlinear blocks, linear block, and noise model. Previous research [24]…”

Section: Learning Approach Of Neural Fuzzy Hammerstein-wiener System With Moving Average Noisementioning

confidence: 99%

Data‐driven Learning Algorithm of Neural Fuzzy Based Hammerstein‐Wiener System

Luo

et al. 2021

Journal of Sensors

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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.

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“…The tasks of parameter learning method are to estimate Hammerstein-Wiener system parameters, that is, two nonlinear blocks, linear block, and noise model. Previous research [24]…”

Section: Learning Approach Of Neural Fuzzy Hammerstein-wiener System With Moving Average Noisementioning

confidence: 99%

Data‐driven Learning Algorithm of Neural Fuzzy Based Hammerstein‐Wiener System

Luo

et al. 2021

Journal of Sensors

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“…The center, c output l , and the width, s output l , can be computed using the cluster method previously proposed. 40 To this end, the key challenge is how to estimate the weight, w output l . The output signals y 1 (k) and y 2 (k), corresponding to two sets of input signals with different sizes, are used.…”

Section: Learning the Static Output Nonlinear Block Parametersmentioning

confidence: 99%

“…The center, c l o u t p u t , and the width, σ l o u t p u t , can be computed using the cluster method previously proposed. 40 To this end, the key challenge is how to estimate the weight, w l o u t p u t .…”

Section: Parameter Learning Of the Neuro-fuzzy Hammerstein–wiener Modelmentioning

confidence: 99%

Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance

Chen

et al. 2020

Measurement and Control

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In this paper, a novel modeling and parameter learning method for the Hammerstein–Wiener model with disturbance is proposed, and the Hammerstein–Wiener model is implemented to approximate complex nonlinear industrial processes. The proposed Hammerstein–Wiener model has two static nonlinear blocks represented by two independent neuro-fuzzy models that surround a dynamic linear block described by the finite impulse response model. The parameter learning method of the Hammerstein–Wiener model with disturbance can be summarized in the following three steps: First, the designed input signals are implemented to completely separate the parameter learning problem of output nonlinear block, linear block, and input nonlinear block. Meanwhile, the static output nonlinear block parameters can be learned based on input and output data of two sets of separable signals with different sizes. Second is to determine the dynamic linear block parameter using correlation analysis algorithm using one set of separable signal; thus, the process disturbance can be compensated by the calculation of correlation function. The final one is to achieve unbiased estimation of the static input nonlinear block parameters using least squares method according to the input–output data of random signal. Furthermore, with the parameter learning method, the proposed model can achieve less computation complexity and good robustness. The simulation results of two cases are provided to demonstrate the advantage of the proposed modeling and parameter learning method.

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“…One of the most efficient interpolation methods is using GPs [23]. The ordered pairs of functions can also be used as rules of the fuzzy system in neuro‐fuzzy modelling of the static functions [27].…”

Section: Preliminaries and Problem Statementmentioning

confidence: 99%