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Nonlinear system identification : NARMAX methods in the time, frequency, and spatio-temporal domains
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice.
Includes coverage of:
* The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model
* The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term
* Statistical and qualitative model validation methods that can be applied to any model class
* Generalised frequency response functions which provide significant insight into nonlinear behaviours
* A completely new class of filters that can move, split, spread, and focus energy
* The response spectrum map and the study of sub harmonic and severely nonlinear systems
* Algorithms that can track rapid time variation in both linear and nonlinear systems
* The important class of spatio-temporal systems that evolve over both space and time
* Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included
to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems
NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems.
This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.
eBook
The short-term effects of air pollutants on hospitalizations for respiratory disease in Hefei, China
Previous studies have shown that ambient air pollution is associated with respiratory morbidity. However, the effects of air pollutants on health have rarely been studied in China. Our study aimed to estimate the short-term effects of particulate air pollutants on hospitalizations for three types of respiratory disease: pneumonia, chronic obstructive pulmonary disease (COPD), and asthma. We collected data on daily admissions for patients with each disease from the New Rural Cooperative Medical System (NRCMS) in Hefei, China. Daily records of air pollutants and meteorological data from January 2014 to March 2016 were also obtained. Distributed lag nonlinear models were employed in the analysis to evaluate the association between daily air pollutants and admissions. The highest effect of each pollutant on COPD hospital admission was observed with PM2.5 at lag 12 (RR = 1.068, 95% CI 1.017 to 1.121) and PM10 at lag 10 (RR = 1.031, 95% CI 1.002 to 1.060), for an increase of 10 μg/m3 in concentrations of the pollutants. The short-term effects of PM10 on asthma hospital admissions peaked at lag 12 (RR = 1.057, 95% CI 1.010 to 1.107). According to our stratified analysis, we found that the effects on COPD admission were more pronounced in the warm season than in the cold season, and the elderly (≥ 65 years) and females were more vulnerable to air pollution.
Journal Article
Nonlinear circuit simulation and modeling : fundamentals for microwave design
\"Discover the nonlinear methods and tools needed to design real-world microwave circuits with this tutorial guide. Balancing theoretical background with practical tools and applications, it covers everything from the basic properties of nonlinear systems such as gain compression, intermodulation and harmonic distortion, to nonlinear circuit analysis and simulation algorithms, and state-of-the-art equivalent circuit and behavioral modeling techniques\"-- Provided by publisher.
Book
Impact of ambient temperature on hospital admissions for cardiovascular disease in Hefei City, China
Many studies have quantified the hospitalization risk for cardiovascular disease (CVD) caused by temperature, but the results of most studies are not consistent. In this study, we evaluate the effect of temperature on CVD hospitalizations. We use a quasi-Poisson regression with a distributed-lag nonlinear model (DLNM) to evaluate the effect of temperature on CVD hospitalizations between July 1, 2015, and October 31, 2017, in Hefei City, China. We found that the cold effect and heat effect of temperature can impact CVD hospital admissions. Compared with the 25th percentile of temperature (10.3 °C), the cumulative relative risk (RR) of extremely low temperature (first percentile of temperature, 0.075 °C) over lags 0–27 days was 0.616 (95% CI 0.423–0.891), and the cumulative RR of moderate low temperature (10th percentile of temperature, 5.16 °C) was 1.081 (95% CI 1.019–1.147) over lags 0–7 days. Compared with the 75th percentile of temperature (25.6 °C), the cumulative RR of extremely high temperature (99th percentile of temperature, 33.7 °C) was 1.078 (95% CI 0.752–1.547) over lags 0–27 days, and the cumulative RR of moderate-high temperature (90th percentile of temperature, 29.0 °C) was 1.015 (95% CI 0.988–1.043) over lag 0 day. In the subgroup, the < 65-year group and male were more susceptible to low temperature; however, the ≥ 65-year group and female were more vulnerable to high temperature. The high temperature’s impact on CVD hospital admissions was found to be more obvious in female and the ≥ 65-year group compared to male and the < 65-year group. However, the < 65-year group and men are more sensitive to low temperature.
Journal Article
High-temperature exposure and risk of spontaneous abortion during early pregnancy: a case–control study in Nanjing, China
As one of the most common complications of early pregnancy, spontaneous abortion is associated with environmental factors, but reports estimating the effect of ambient temperature on spontaneous abortion are still inconclusive. Herein, a case–control study (1002 cases and 2004 controls) in Nanjing, China, from 2017 to 2021 was conducted to evaluate the association between temperature exposure and the risk of spontaneous abortion by using distributed lag nonlinear model (DLNM). As a result, daily mean temperature exposure and early spontaneous abortion showed a nonlinear relationship in 14-day lag periods. Moreover, taking the median temperature (17 °C) as a reference, gradually increased positive effects of high temperature on spontaneous abortion could be found during the 4 days prior to hospitalization, and the highest odds ratio (OR) of 2.07 (95% confidence interval (CI): 1.36, 3.16) at extremely hot temperature (33 °C) was observed at 1 lag day. The results suggested that high-temperature exposure in short times during early pregnancy might increase the risk of SAB. Thus, our findings highlight the potential risk of short-term high-temperature exposure during early pregnancy, and more evidence was given for the effects of climate change on maternal health.
Journal Article
Neural network-based nonlinear model predictive control with anti-dead-zone function for magnetic shape memory alloy actuator
Magnetic shape memory alloy-based actuator (MSMA-BA) has the advantages of large strain and high resolution. However, the inherent hysteresis characteristics accompanied by the dead zone in MSMA seriously degrade the positioning accuracy of MSMA-BA. In this study, a gated recurrent neural network (GRNN)-based nonlinear model predictive control (NMPC) method is designed to achieve precise trajectory tracking control of the MSMA-BA. First, a GRNN-based nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model is designed to predict the various nonlinear characteristics of MSMA-BA. Based on the established model, an NMPC method with an anti-dead-zone function is designed. The introduced anti-dead-zone function enables the proposed NMPC algorithm to accelerate the response speed within the dead zone and prevents violent oscillations in the system. The ability of the NMPC to address the hysteresis characteristics accompanied by the dead zone is enhanced. Additionally, the convergence of the proposed NMPC method is analyzed using the Lyapunov stability theory. Extensive experiments are conducted on the MSMA-BA to validate the effectiveness of the proposed method.
Journal Article
Short-term exposure to temperature and mental health in North Carolina: a distributed lag nonlinear analysis
Adverse mental health outcomes have been associated with high temperatures in studies worldwide. Few studies explore a broad range of mental health outcomes, and to our knowledge, none are specific to NC, USA. This ecological study explored the relationship between ambient temperature and mental health outcomes (suicide, self-harm and suicide ideation, anxiety and stress, mood disorders, and depression) in six urban counties across the state of NC, USA. We applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM) to examine the short-term effects of daily ambient temperature on emergency admissions for mental health conditions (2016 to 2018) and violent deaths (2004 to 2018). The results were predominately insignificant, with some key exceptions. The county with the greatest temperature range (Wake) displays higher levels of significance, while counties with the lowest temperature ranges (New Hanover and Pitt) are almost entirely insignificant. Self-harm and suicidal ideation peak in the warm months (July) and generally exhibit a protective effect at lower temperatures and shorter lag intervals. Whereas anxiety, depression, and major depressive disorders peak in the cooler months (May and September). Suicide is the only outcome that favored a 20-day lag period in the sensitivity analysis, although the association with temperature was insignificant. Our findings suggest additional research is needed across a suite of mental health outcomes to fully understand the effects of temperatures on mental health.
Journal Article