Helen Shin Huey Wee, Nur Syazreen Ahmad^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1301-1354, 2025, DOI:10.32604/cmes.2025.063611 - 30 May 2025

Abstract This paper reviews recent advancements in system identification methods for perturbed motorized systems, focusing on brushed DC motors, brushless DC motors, and permanent magnet synchronous motors. It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms, highlighting their advantages, limitations, and applications. The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads, noise, and friction. A comparative performance analysis is also included to assess several widely used optimization methods, including least squares (LS), particle swarm optimization (PSO), grey wolf optimizer (GWO), bat algorithm… More >

Rakesh Kumar Pattanaik¹, Mihir N. Mohanty^1,*, Srikanta Ku. Mohapatra², Binod Ku. Pattanayak³

Computer Systems Science and Engineering, Vol.46, No.1, pp. 195-208, 2023, DOI:10.32604/csse.2023.029591 - 20 January 2023

Abstract System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems. As most practical systems don’t have prior information about the system behaviour thus, mathematical modelling is required. The authors have proposed a stacked Bidirectional Long-Short Term Memory (Bi-LSTM) model to handle the problem of nonlinear dynamic system identification in this paper. The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions. The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:… More >

M. Rajalakshmi¹, V. Saravanan², V. Arunprasad³, C. A. T. Romero⁴, O. I. Khalaf⁵, C. Karthik^1,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 339-359, 2022, DOI:10.32604/iasc.2022.021696 - 26 October 2021

Abstract In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because the sugar industry’s clarification process is difficult and nonlinear, obtaining the exact model using identification methods is critical. For regulating the clarification process and identifying the model parameters, this work presents a state transition algorithm (STA). First, the model parameters for the clarifier are estimated using the normal system identification process. The STA is then utilized to improve the accuracy of the system parameters that have been identified. Metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm More >

Chung Wen Hung, Wei Lung Mao, Han Yi Huang

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 329-341, 2019, DOI:10.31209/2019.100000093

Abstract Nonlinear system modeling and identification is the one of the most important areas in engineering problem. The paper presents the recurrent fuzzy neural network (RFNN) trained by modified particle swarm optimization (MPSO) methods for identifying the dynamic systems and chaotic observation prediction. The proposed MPSO algorithms mainly modify the calculation formulas of inertia weights. Two MPSOs, namely linear decreasing particle swarm optimization (LDPSO) and adaptive particle swarm optimization (APSO) are developed to enhance the convergence behavior in learning process. The RFNN uses MPSO based method to tune the parameters of the membership functions, and it More >

Ching‐Yi Chen, Yi‐Jen Lin

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 319-327, 2019, DOI:10.31209/2019.100000093

Abstract In this study, a systematic data-driven adaptive neuro-fuzzy inference system (ANFIS) modelling methodology is proposed. The new methodology employs an unsupervised competitive learning scheme to build an initial ANFIS structure from input-output data, and a high-performance PSO-LSE method is developed to improve the structure and to identify the consequent parameters of ANFIS model. This proposed modelling approach is evaluated using several nonlinear systems and is shown to outperform other modelling approaches. The experimental results demonstrate that our proposed approach is able to find the most suitable architecture with better results compared with other methods from More >

Vinay Shimpi¹, Madappa V. R. Sivasubramanian^1,*, S. B. Singh²

Structural Durability & Health Monitoring, Vol.13, No.1, pp. 1-40, 2019, DOI:10.32604/sdhm.2019.05951

Abstract In this review article, the past investigations carried out on heritage structures using Ambient Vibration Test (AVT) and Operational Modal Analysis (OMA) for system identification (determination of dynamic properties like frequency, mode shape and damping ratios) and associated applications are summarized. A total of 68 major research studies on heritage structures around the world that are available in literature are surveyed for this purpose. At first, field investigations carried out on heritage structures prior to conducting AVT are explained in detail. Next, specifications of accelerometers, location of accelerometers and optimization of accelerometer networks have been More >

A. Rama Mohan Rao¹, K. Lakshmi¹, Karthik Ganesan²

Structural Durability & Health Monitoring, Vol.9, No.2, pp. 99-128, 2013, DOI:10.32604/sdhm.2013.009.099

Abstract Development of efficient system identification techniques is highly relevant for large civil infrastructure for effective health monitoring, damage detection and vibration control. This paper presents a system identification scheme in time domain to estimate stiffness and damping parameters of structures using measured acceleration. Instead of solving the system identification problem as an inverse problem, we formulate it as an optimisation problem. Particle swarm optimisation (PSO) and its other variants has been a subject of research for the past few decades for solving complex optimisation problems. In this paper, a dynamic quantum behaved particle swarm optimisation… More >

Leandro Fleck Fadel Miguel¹, Letícia Fleck Fadel Miguel²

Structural Durability & Health Monitoring, Vol.8, No.3, pp. 249-270, 2012, DOI:10.32604/sdhm.2012.008.249

Abstract Model updating may be defined as an adjustment on the FE model through modal parameters experimentally obtained, in order to better represent its dynamic behavior. From this definition, structural health monitoring (SHM) methods can be considered closely related with these procedures, because it refers to the implementation of in situ non-destructive sensing and analysis of the dynamic system characteristics, which aims to detect changes that could indicate damage. Within this context, the present paper evaluates an iterative model updating approach when it is subjected to experimental vibration data. In addition, after getting the experimental adjusted… More >

S.Mohanty¹, A. Chattopadhyay², J. Wei³, P. Peralta⁴

Structural Durability & Health Monitoring, Vol.5, No.3, pp. 227-250, 2009, DOI:10.3970/sdhm.2009.005.227

Abstract This paper proposes unsupervised system identification based methods to estimate time-series fatigue damage states in real-time. Ultrasound broadband input is used for active damage interrogation. Novel damage index estimation techniques based on dual sensor signals are proposed. The dual sensor configuration is used to remove electrical noise, as well as to improve spatial resolution in damage state estimation. The scalar damage index at any particular damage condition is evaluated using nonparametric system identification techniques, which includes an empirical transfer function estimation approach and a correlation analysis approach. In addition, the effectiveness of two sensor configurations More >

R Sivaprasad^1,2, S Venkatesha¹, C S Manohar^1,3

CMC-Computers, Materials & Continua, Vol.9, No.3, pp. 179-208, 2009, DOI:10.3970/cmc.2009.009.179

Abstract The problem of identifying parameters of time invariant linear dynamical systems with fractional derivative damping models, based on a spatially incomplete set of measured frequency response functions and experimentally determined eigensolutions, is considered. Methods based on inverse sensitivity analysis of damped eigensolutions and frequency response functions are developed. It is shown that the eigensensitivity method requires the development of derivatives of solutions of an asymmetric generalized eigenvalue problem. Both the first and second order inverse sensitivity analyses are considered. The study demonstrates the successful performance of the identification algorithms developed based on synthetic data on More >