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

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.

Modeling and simulating dynamics of lithium-ion batteries using block-oriented models with piecewise linear static nonlinearity

The article deals with the research of the efficiency of modelling the dynamics of voltage change in lithium-ion rechargeable batteries in charging/discharging modes using nonlinear block-oriented systems. Drawing on experimental data, a structural and parametric identification of the Hammerstein, Wiener and Hammerstein-Wiener models with a polynomial structure of the linear dynamic block and piecewise linear static nonlinearities was performed. It has been established that the best modelling accuracy was ensured by using the Hammerstein-Wiener system with a linear model having the 6th order of the numerator and denominator polynomials and an input delay of 3 samples. It showed 15.67% and 6.2% higher accuracy compared to the Wiener and Hammerstein systems, respectively. The application of those models in battery management systems will make it possible to improve the control quality for battery assemblies of solar and wind power plants in the context of the variable nature of the charging/discharging processes due to the variability of weather conditions and fluctuations in power consumption during a 24-hour period. This will ensure a wider introduction of renewable power generation into existing power systems, which is currently the leading way to ensure sustainable development of the energy sector.

Analyzing the nonlinear system by designing an optimum digital filter named Hermitian-Wiener filter

The classical Wiener filter was engaged into identifying the linear structures, resulting in clear and incredible drawbacks in working with nonlinear integrated system. Currently, the Hermitian-Wiener system are suitable for unpredicted sub-system that consists of numerous and complex inputs. The system introduces a two-stage to analyze the subintervals where the output nonlinearities are noninvertible, through using the unknown orders and parameters. Finally, a practical strategy would be discussed to analyze the nonlinear parameters.