The Levenberg–Marquardt (LM) method is one of the most significant methods for solving nonlinear equations as well as symmetric and asymmetric linear equations. To improve the method, this paper proposes a new adaptive LM algorithm by modifying...
This research investigates the application of supercritical carbon dioxide (CO2) within carbon capture, utilization, and storage (CCUS) technologies to enhance oil-well production efficiency and facilitate carbon storage, thereby promoting a low-carb...
This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB) electric model by using a combination of particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithms. Two-dimensional Poisson equatio...
In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate gradient (SCG) algorithm...
Understanding the flashover performance of the outdoor high voltage insulator has been in the interest of many researchers recently. Various studies have been performed to investigate the critical flashover voltage of outdoor high voltage insulators...
This paper investigates a novel recurrent neural network (NN)-based vector control approach for single-phase grid-connected converters (GCCs) with L (inductor), LC (inductor-capacitor) and LCL (inductor-capacitor-inductor) filters and provides their...
In this paper, a nonlinear least squares optimization method is employed to optimize the performance of pole-zero-cancellation (PZC)-based digital controllers applied to a switching converter. An extensively used step-down converter operating at 1000...
Pipelines are exposed to the severe threat of natural disasters, where the damage caused by landslides are particularly bad. Hence, in the route arrangement and maintenance management of pipeline projects, it is particularly important to evaluate the...
During natural gas development, the gas–water two-phase flows in upward and horizontal wellbores are complex and variable. The accurate calculation of the water holdup in each production layer using appropriate methods based on the logging data...
Industrial aluminum-block heating processes exhibit nonlinear dynamics, substantial time delays, and stringent requirements for fault detection and diagnosis, especially in semiconductor manufacturing and other high-precision electronic processes, wh...
This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate...
Traditional field surveys are expensive, time-consuming, laborious, and difficult to perform, especially in mountainous and dense forests, which imposes a burden on forest management personnel and researchers. This study focuses on predicting forest...
The problem of overlapping peaks has been a challenge in microchip electrophoresis (ME) signal analysis. However, traditional peak fitting algorithms have difficulty analyzing overlapping peaks due to the high dependence on the starting point. In thi...
The optimal performance of a hydroelectric power plant depends on accurate monitoring and well-functioning sensors for data acquisition. This study proposes the use of artificial neural networks (ANNs) to estimate the Pelton turbine shaft power of a...
A new method for extracting the Brillouin frequency shift (BFS) from the Brillouin gain spectrum (BGS), the modified backward correlation method (MBWC), is presented. The possibilities of using MBWC, and MBWC in combination with the Lorentzian curve...
Welding alloy 617 with other metals and alloys has been receiving significant attention in the last few years. It is considered to be the benchmark for the development of economical hybrid structures to be used in different engineering applications....
Regression analysis is widely applied in many fields of science to estimate important variables. In general, nonlinear regression is a complex optimization problem and presents intrinsic difficulties in estimating reliable parameters. Nonlinear optim...
Climate change continues to pose significant challenges to global water security, with dams being particularly vulnerable to hydrological cycle alterations. This study investigated the climate-based impact on the hydrology of the Doğancı da...
Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by the algorithm is trained with engineering data. In this work, we analyzed the...
This paper develops a concentration retrieval technique based on the particle swarm optimization (PSO) algorithm, which is used for a calibration-free wavelength modulation spectroscopy system. As compared with the commonly used technique based on th...
A data-fitting method based on the whale optimization algorithm (WOA) is proposed to determine the thickness and refractive index of films measured by spectroscopic ellipsometry (SE). To demonstrate this method, tin oxide (SnO2) films with transparen...
This study presents an integrated approach that combines the Levenberg–Marquardt (LM) and Luus–Jaakola (LJ) algorithms to enhance parameter estimation for various applications. The LM algorithm, known for its precision in solving non-line...
Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contami...
In this paper, a mathematical model for the rolling motion of ships in random beam seas has been investigated. The ships’ steady-state rolling motion with a nonlinear restoring moment and damping effect is modeled by the nonlinear second-order...
Smart social systems are ones where a number of individuals share and interact with each other via various networking devices. There exist a number of benefits to including smart-based systems in networks such as religions, economy, medicine, and oth...
Accurate parameter estimation is essential for effective monitoring and treatment of high-salinity industrial wastewater. Traditional methods such as spectroscopy, ion chromatography, and electrochemical analysis offer high sensitivity but are often...
Population growth, industrialization, excessive energy consumption, and deforestation have led to climate change and affected water resources like dams intended for public drinking water. Meteorological parameters could be used to understand these ef...
To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg&ndas...
To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM...
Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of pre...
State of charge (SOC) estimation is becoming increasingly important, along with electric vehicle (EV) rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring t...
Magnetic detection technology has wide applications in the fields of geological exploration, biomedical treatment, wreck removal and localization of unexploded ordinance. A large number of methods have been developed to locate targets with static mag...
Indoor localization algorithms based on the received signal strength indicator (RSSI) in wireless sensor networks (WSNs) have higher localization accuracy than other range-free methods. This paper considers indoor localization based on multilateratio...
With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Ma...
Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye–Einstein integrals in the temperature range from 0K to 300K. The parameters of the Debye–Einstein approach are either obtained by a Marko...
Data mining technology is applied to extract the water supply operation rules in this study. Five characteristic attributes—reservoir storage water, operation period number, water demand, runoff, and hydrological year—are chosen as the dataset, and t...
Imprecise measurements present universally due to variability in the measurement error. We devised a very simple membership function to evaluate fuzzily the quality of optical sensing with a small dataset, where a normal distribution cannot be assume...
The fluxgate magnetometer has the advantages of having a small volume and low power consumption and being light weight and is commonly used to detect weak magnetic targets, including ferrous metals, unexploded bombs (UXOs), and underground corrosion...
This study investigates the effectiveness of Artificial Neural Networks (ANNs) in predicting the outcomes of Double Cantilever Beam (DCB) tests, focusing on time and force as input variables and displacement as the predicted output. Three ANN trainin...
Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deploy...
In this study, we have developed a platform which incorporates Artificial Neural Networks (ANNs) in simulating body dynamics of mobile ground vehicles (e.g. cars). This is a part of our research project in which we plan to provide a platform for educ...
In this study, a hybrid method based on coupling discrete wavelet transforms (DWTs) and artificial neural network (ANN) for yield spread forecasting is proposed. The discrete wavelet transform (DWT) using five different wavelet families is applied to...
Relocalization technology is an important part of autonomous vehicle navigation. It allows the vehicle to find its position on the map after a reboot. This paper presents a relocalization algorithm framework that uses image retrieval techniques. An i...
The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was...
A multi-robot collaboration system can complete more complex tasks than a single robot system. Ensuring the calibration accuracy between robots in the system is a prerequisite for the effective inter-robot cooperation. This paper presents a dual-robo...
This paper emphasizes the design and investigation of a new optimization scheme for a grid-connected photovoltaic system (PVS) under unbalance faults. The proposed scheme includes fuzzy logic controller (FLC) based on the Levenberg–Marquardt (L...
Magnetic anomaly data rapidly decay with distance and are susceptible to environmental magnetic noise, which leads to reduced accuracy and robustness in estimating magnetic source parameters. This shows significant differences between estimated and t...
The aim of this paper is to establish a reliable model that provides the best fit to the specific behavior of the flow stresses of the 10%Cr steel alloy at the time of hot deformation. Modified Johnson–Cook and strain-compensated Arrhenius-type...
In this paper, force sensor signals are classified using a pattern recognition neural network (PRNN). The signals are classified to show if there is a collision or not. In our previous work, the joints positions of a 2-DOF robot were used to estimate...
Accurate kinematic calibration of subreflector actuators is essential for pointing precision of large radio telescopes, particularly at high frequencies. Conventional least-squares methods are vulnerable to noise and outliers, and their accuracy may...
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