Artificial Neural Network Algorithm Research Articles

Analysis of Spectral Characteristics of Cotton Leaves at Bud Stage under Different Nitrogen Application Rates

Soil salinity affects nutrient uptake by cotton. The cotton bud stage is a very important period in the process of cotton planting and directly affects the yield of cotton. The nutritional status of the bud stage directly affects the reflectance spectra of cotton canopy leaves. Therefore, it is of great significance to nondestructively monitor the nutritional status of the cotton bud stage on salinized soil via spectroscopic techniques and perform corresponding management measures to improve cotton yield. In this study, potted plants with different nitrogen application rates were set up to obtain the reflection spectral curves of cotton bud stage leaves, analyze their spectral characteristics under different nitrogen application rates, and establish spectral estimation models of chlorophyll density. The results are as follows: in the continuum removal spectrum of the cotton bud stage, the lowest point of the absorption valley near 500 nm shifted to the shortwave direction with an increasing nitrogen application rate. The mean reflectance between 765 and 880 nm was significantly different between nitrogen-stressed and nitrogen-unstressed cotton. The average reflectance of the near-infrared band, the absorption valley depths near 500 nm and 675 nm, the first derivative of the 710 nm reflectance, and the second derivatives of the 690 nm and 730 nm reflectance increased with increasing nitrogen application and chlorophyll density, and significant correlations were observed with the chlorophyll density. These parameters were modeled using support vector regression (SVR) and artificial neural network (ANN) methods, two commonly used algorithms in the field of machine learning. The determination coefficients of the three chlorophyll samples via the ANN models were 0.92, 0.77, and 0.94 for the modeling set and 0.77, 0.69, and 0.77 for the verification set. The ratio of quartile to root-mean-square error (RPIQ) of the ANN model was greater than 2.2, and the ratio of the standard error of the measured value to the standard error of the predicted (SEL/SEP) was close to 1, indicating that the chlorophyll density estimation models built based on the ANN algorithm had robust prediction ability. Our model could accurately estimate the leaf chlorophyll density in the cotton bud stage.

Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset

BackgroundUnder-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors.MethodsThe study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew’s correlation coefficient, and the Area Under the Receiver Operating Characteristics.ResultsThe study found that machine learning models can accurately predict under-five mortality, with the Random Forest and Artificial Neural Network algorithms emerging as the best models, both achieving an accuracy of 89.47% and an AUROC of 96%. The results show that under-five mortality rates vary significantly across different characteristics, with wealth index, maternal education, antenatal visits, place of delivery, employment status of the woman, number of children ever born, and region found to be the top determinants of under-five mortality in Nigeria.ConclusionsThe findings suggest that machine learning models can be useful in predicting U5M in Nigeria with high accuracy. The study emphasizes the importance of addressing social, economic, and demographic disparities among the population in Nigeria. The study’s findings can inform policymakers and health workers about developing targeted interventions to reduce under-five mortality in Nigeria.

Understanding and Improving Optimization in Predictive Coding Networks

Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible. In contrast, the standard learning algorithm for predictive coding (PC) models in neuroscience, known as the inference learning algorithm (IL), is a promising, bio-plausible alternative. However, several challenges and questions hinder IL's application to real-world problems. For example, IL is computationally demanding, and without memory-intensive optimizers like Adam, IL may converge to poor local minima. Moreover, although IL can reduce loss more quickly than BP, the reasons for these speedups or their robustness remains unclear. In this paper, we tackle these challenges by 1) altering the standard implementation of PC circuits to substantially reduce computation, 2) developing a novel optimizer that improves the convergence of IL without increasing memory usage, and 3) establishing theoretical results that help elucidate the conditions under which IL is sensitive to second and higher-order information.

Creation of a Software Tool with Elements of an Artificial Neural Network for Data Forecasting

Currently, Russia is moving towards automating the collection of information from metering devices for heat, gas, water, electricity, etc. Technologies are being developed that allow transmitting data on resource consumption through various communication channels, both wired and on-air. There are already devices and devices that allow transmitting readings of electricity metering devices over existing wired communication networks, and this issue is being resolved at the legislative level, federal Law No. 522-FZ of 01.07.2020 has been adopted. The basis for the research is to save time and effort to obtain the necessary information to take into account the readings of electricity from substations, increase the speed of decision-making, to be able to predict the situation with big data to identify abnormal values and eliminate them (for example, identified data from electricity meters will reduce the time labor costs of accounting operators and economic losses of the energy supply company). The article proposes software methods for detecting data on electricity losses based on the use of artificial neural network algorithms and allowing to detect inconsistencies of commercial data readings of electric meters, which will reduce the commercial component of electricity losses. A verification calculation of an artificial neural network was performed for inconsistencies in the data on the transmitted electricity.

Bank Customer Churn Prediction Using SMOTE: A Comparative Analysis

In today's market, customers have a plethora of options available to them when deciding where to invest their money. Consequently, customer churn and engagement have emerged as prominent concerns. With an increasing number of service providers targeting the same customer base, it is imperative for providers to understand evolving customer behavior and heightened expectations to retain their clientele. Numerous studies have addressed the issue of customer churn, with data mining frequently employed to predict bank customer attrition. While many researchers have proposed various approaches for predicting customer churn, some machine learning (ML) algorithms have struggled to deliver the required performance in identifying customer churn accurately most especially when the dataset is imbalance data. Therefore, this paper presents an application of Synthetic Minority Over Sampling Technique (SMOTE) on bank churn dataset. The SMOTE algorithm was employed to address the problem of data imbalance and Genetic Algorithm (GA) was applied to select most informative features from the original dataset. The selective features were evaluate using four (4) different classification algorithms: Random Forest (RF), K-Nearnest Neighbor (KNN), Artificial Neural Network (ANN) and Adaboost algorithms. The KNN model demonstrated superior performance compared to other models in terms of accuracy (96%), precision (96%), and F-measure (96%) respectively. Furthermore, we compared our results with existing models that utilized the same dataset, and our proposed strategy outperformed them.

Implementing machine learning in robotic wire arc additive manufacturing for minimizing surface roughness

ABSTRACT Wire arc additive manufacturing (WAAM) has emerged as an important type of metal additive manufacturing technique in recent years. The most critical challenge in WAAM is to achieve the desired surface roughness of deposited layers. The improvement of surface roughness can play an important role in improving the quality of parts made using WAAM. In this study, three machine learning (ML) algorithms, such as artificial neural network (ANN), random forest (RF) and XGboost algorithm, have been used to predict the surface roughness of deposited beads. A dataset for training and testing the ML models has been obtained using a 3D optical profilometer by measuring the surface roughness of a single-layer deposited bead of aluminium alloy. The performance of ML algorithms was evaluated using statistical metrics such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). Tuning of hyperparameters helped in improving the performance of ML models. Among the three ML algorithms, the XGBoost and ANN algorithms outperformed RF in predicting the roughness of WAAM deposited parts. This novel approach can help industries in the modelling of process parameters and easy implementation of WAAM with minimal resources, just with the use of real-time data.

'Marfim pattern’ of wood species in the Southern Brazilian timber market: colorimetry and visible spectra

ABSTRACT Evaluating wood with similar characteristics is a challenging task. Taking into consideration the need for an efficient and rapid technique, the use of visible spectrophotometry to discriminate wood species is proposed. This study aimed to differentiate nine wood species with a similar yellowish color using visible spectrophotometry, associated with two classification models, to verify predictions. Color data were obtained from different anatomical sections of the wood, along with reflectance spectra in the range of 360–740 nm. Two machine learning approaches were applied: artificial neural network (ANN) and k-nearest neighbors (k-NN). In general, the mean spectra of the samples shared similar information, as they are materials of lignocellulosic origin. However, in all tests, the models built using the ANN algorithm demonstrated an accuracy in discriminating species exceeding 97%. Notably, when applying ANN to the spectra obtained solely from the longitudinal surface, superior results were obtained. Discriminative patterns can be obtained by visible spectra independent of the anatomical section. The integration of visible spectra and ANN proved to be a suitable approach to recognize “marfim pattern” wood.

A High-Accuracy Blood Glucose Detection Sensor Using Tunable Bandpass Filter and MLP and RBF Artificial Neural Network Algorithms

A High-Accuracy Blood Glucose Detection Sensor Using Tunable Bandpass Filter and MLP and RBF Artificial Neural Network Algorithms

Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging.

Direct recycling of aluminum waste is crucial in sustainable manufacturing to mitigate environmental impact and conserve resources. This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. The experimental runs were designed using Full factorial and RSM via Minitab 21 software. RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. The chips of different morphology were cold compressed into billet form and then hot forged. The effect of varying forging temperature (Tp, 450-550°C), holding time (HT, 60-120 minutes), and chip surface area to volume ratio (AS:V, 15.4-52.6 mm2/mm3) on ultimate tensile strength (UTS) was examined. Maximum UTS (237.4 MPa) was achieved at 550°C, 120 minutes and 15.4 mm2/mm3 of chip's AS: V. The Tp had the largest contributing effect ratio on the UTS, followed by HT and AS:V according to ANOVA analysis. The proposed optimization process suggested 550°C, 60 minutes, and 15.4 mm2 as the optimal condition yielding the maximum UTS. The developed models' evaluation results showed that ANN (with MSE = 1.48%) outperformed RSM model. Overall, the study promotes sustainable production by demonstrating the potential of integrating RSM and ML to optimize complex manufacturing processes and improve product quality.

Hedging policy using neural networks and its combination with heuristic algorithms case study: Dez Reservoir

In recent years, the use of various Artificial Intelligence (AI) methods, such as evolutionary computation, heuristic algorithms, artificial neural networks, and fuzzy theory calculations, has gained popularity in addressing water resources issues. These algorithms have shown great success in solving problems that traditional deterministic methods struggle with. This study focuses on optimizing Dez reservoir operation over a long-term period using a nonlinear loss function through an evolutionary artificial neural network algorithm. The outcomes of this approach are then contrasted with genetic exploration and harmony search algorithms, highlighting the strengths and weaknesses of each method. Ultimately, a combination of the evolutionary artificial neural network method and hedging models is employed for optimal reservoir management, with results compared to the previous approach. Results show the appropriate performance of combining hedging policy with artificial neural network and harmony search algorithm. This combination significantly reduces the vulnerability value with a slight decrease in reliability.

Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria

Due to the extremely poor efficiency of solar energy, researchers created various maximum power point tracking (MPPT) techniques with the aim of enhancing the effectiveness of photovoltaic (PV) systems. Because of their propensity to address complex problems and their non-linear characteristics, Artificial Neural Network (ANN) algorithms are the most frequently used among these MPPT techniques. Nevertheless, the performance of the ANN-based MPP tracking algorithms is contingent upon various factors, including the choice of activation function, the quantity of hidden neurons, and the training algorithm employed. Shannon’s Information Criteria (SIC) is used to determine the optimal number of hidden neurons within a single hidden layer for the neuro-controller application. In this regard, a two-layer Feed-Forward Neural Network (FFNN) was trained using MATLAB/Simulink software, incorporating varying numbers of hidden neurons. The results indicate that the two-layer FFNN with five hidden neurons has the highest performance, as demonstrated by the lowest Mean Squared Error (MSE) of 4.03 × 10-9, which is statistically significant. The successful incorporation of Gaussian noise in the simulation of an 85 kW PV system demonstrates that the ANN-based MPPT algorithm is both theoretically robust and practically viable and reliable for enhancing the efficiency of solar PV systems in real-world scenarios.

Diabetes Prediction with Machine Learning with Python

This article introduces an innovative approach leveraging a combination of machine learning techniques to enhance early diabetes detection, a crucial step given the disease's global impact. With the prevalence of sugar and fats in contemporary diets contributing to an increased diabetes risk, early identification through symptom recognition is key. The proposed method integrates Using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms, patient data is analyzed to classify diabetes diagnoses as either affirmative or negative. The study involves the utilization of a dataset that has been divided into 70% for training data and 30% for testing data. The outputs from the SVM and ANN models serve as inputs for a fuzzy logic system, which then makes the final diagnosis determination. This hybrid model is stored on a cloud platform for accessibility and uses real-time patient data for predictions. The combined machine learning model demonstrates superior accuracy in predicting diabetes compared to existing methods.

Cross-physical field prediction method for smoke field distribution in commercial building fire based on distributed optical fiber sensor

In modern commercial building spaces, the smoke generated during indoor fires poses a substantial safety threat to individuals attempting to evacuate. This study leveraged a distributed optical fiber temperature sensing system as the technological foundation. A series of commercial building space models were constructed by employing fire dynamics simulations, and a comprehensive database of simulated fire field information was established. The study built upon this database and proposed a method to establish influence pathways from existing temperature field detection data to three fire risk assessment parameters: carbon dioxide, oxygen, and visibility. The study utilized various machine learning algorithms and focused on selecting and constructing cross-physical field prediction models that optimized the performance of fire risk assessment parameters at the fire key plane. The findings demonstrated that the Multilayer Perceptron (MLP) algorithm model yielded the most accurate fit for carbon dioxide volume fraction, with a Mean Absolute Percentage Error (MAPE) of 3.80% between the model predictions and actual values. The Artificial Neural Network (ANN) algorithm showed superior fitting effects for oxygen volume fraction and visibility, with MAPEs of 1.37% and 3.10%, respectively. Furthermore, the study validated the generalizability of these optimal models by altering spatial scales, revealing robust performance. This study utilized a single optical fiber sensor to achieve dynamic prediction across different physical fields during fire incidents, expanding the application scope of optical fiber sensors in monitoring gas and visibility fields in commercial building fires. This not only enhanced the level of fire monitoring but also effectively reduce equipment costs. It provided effective data support for planning evacuation routes for individuals and rescue arrangements for firefighters, further ensuring personnel safety.

MACHINE LEARNING AND ARTIFICIAL NEURAL NETWORK TECHNOLOGIES IN THE CLASSIFICATION OF POSTKERATOTOMIC CORNEAL DEFORMITY

Backgraund: A thorough analysis of both the optical and anatomical properties of the cornea in patients after anterior radial keratotomy is of particular importance in choosing the optical strength of an intraocular lens in the surgical treatment of cataracts and other types of optical correction. The variability of the clinical picture of postkeratotomic deformity (PCRD) determines the need to develop its classification and is an important task of modern ophthalmology.
 Aims: to develop an automated system of classification of corneal PCRD using machine learning and an artificial neural network based on the analysis of topographic maps of the cornea.
 Materials and methods: depersonalized results of the analysis of medical records of 250 patients aged 59.63±5.95 (from 46 to 76) years were used as the material. The analysis of 500 maps of the relief-topography of the anterior and posterior surfaces of the cornea and 3 stages of machine learning of the PCRD classification were carried out.
 Results: Stage 1- analysis of the relief topography of the anterior and posterior surfaces of the cornea allowed us to fix the numerical values of the elevation of the anterior and posterior surfaces of the cornea in three ring-shaped zones. At stage 2, in the course of deep machine learning, a direct distribution neural network was selected and created. 8 auxiliary parameters describing the shape of the anterior and posterior surfaces of the cornea were established. Stage 3 was accompanied by obtaining algorithms for the classification of PCRD depending on the ratio of test and training samples, which ranged from 75 to 91%..
 Conclusion: The use of artificial neural network algorithms can become a useful tool for automatic classification of postkeratotomic corneal deformity in patients who have previously undergone radial keratotomy.

Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel electrodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms—artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors—to evaluate their ability to predict wear patterns accurately. Each algorithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.

Electricity Load Demand Prediction for Microgrid Energy Management System Using Hybrid Adaptive Barnacle‐Mating Optimizer with Artificial Neural Network Algorithm

In recent years, one of the primary causes of high electricity consumption is the growing global population and the availability of power‐demanding smart devices. As a result, accurate load forecasting tools are required for effective energy conservation in microgrids. Various simulation tools and artificial intelligence (AI)‐based methods have been used to make the best forecasts of electricity demand. Furthermore, conventional systems are static and based solely on historical data. To address this practical need, this work aims to create a machine learning (ML) model for short‐term load forecasting based on feature selection and parameter optimization. This research proposes a hybrid short‐term load demand prediction approach that combines an adaptive barnacle‐mating optimizer (ABMO) and an artificial neural network (ANN). When compared to the regression tree (RT), support vector machine (SVM), ANN, and particle swarm optimization‐based ANN (PSO‐ANN) algorithms, the proposed ABMO‐ANN algorithm reduces mean absolute percentage errors by 67.69%, 64.58%, 59.18%, and 42.02%, respectively. Compared to conventional RT, SVM, ANN, and PSO‐ANN algorithm, the hybrid ABMO with ANN outperforms them based on mean absolute percentage error, root mean square error, correlation coefficient , symmetric mean absolute percentage error, and agreement index (d).

Modeling of hydrogen flow decompression from a storage by a two-stage Tesla valve: A hybrid approach of artificial neural network, response surface methodology, and genetic algorithm optimization

Hydrogen, as an energy carrier, requires efficient handling and regulation, especially during storage and release phases. The efficient hydrogen release from the storage tanks is often coupled with a rapid decompression process. Pressure regulators, hydrogen sensors, and monitoring systems are commonly used to control the pressure of hydrogen gas in storage tanks and distribution systems. Besides the mentioned methods, Tesla valves offer a compelling solution due to their unique design and functionality. In the current research, a novel two-stage Tesla valve was designed to regulate the hydrogen flow in both forward and reverse directions. Artificial neural networks (ANN) and response surface methodology (RSM) were applied to provide the prediction models. Presenting these ready-made and pre-determined models can help researchers and developers of Tesla valves to achieve their goals in the shortest time and with the lowest cost. In this research, two different prediction models were employed to predict the diodicity (Di) and absolute pressure drop ratio (APDR) in the Tesla valve. The distance between stages (X), length of divider (L), valve angle (θ), and depth of divider penetration (H) are the input variables of the prediction models. These variables, as the most influential parameters, are carefully selected and manipulated within specified ranges. Moreover, single- and multi-objective optimizations were performed by genetic algorithm, and the Pareto fronts for Di and APDR were presented. For the first time, the combination of these three methods (ANN, RSM, and genetic algorithm) was employed herein to investigate the decompression performance and hydraulic behavior of Tesla valves. Based on the findings, the ANN models exhibited higher accuracy with R2 values of 0.991 relative to RSM models (for both responses). The optimized design significantly enhanced the pressure drop of hydrogen flow in the reverse direction, resulting in an improvement of about 379.5 % in diodicity. Besides, the novel two-staged Tesla valve enhanced the pressure drop by about 4830 % and 1354 % in reverse and forward directions compared to the simple straight channel.

Penerapan JST Untuk Prakiraan Cuaca Di Wilayah Kota Blitar Menggunakan Metode Algoritma Hopfield

Weather forecasting is the application of science and technology to estimate a state of the atmosphere in the future and will occur in a certain area. In this study, weather forecasting in the Blitar City area uses the Hopfield Algorithm Artificial Neural Network which can be used to identify patterns of numbers and letters using classification techniques. The weather criteria used in this method are wind speed, temperature, humidity and air pressure. The collected morning, afternoon and evening data are classified according to the expected target output, i.e. rainy, sunny and cloudy. From the collection of 21 data that was carried out, it resulted in a match with the target weather of 76% accuracy and an error rate of 24%.

Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm

The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains.

Comparison of different supervised learning algorithms for position analysis of the slider-crank mechanism

Slider-crank mechanisms have a wide range of applications in machine design, primarily for converting rotary motion into reciprocating motion, as seen in internal combustion engines, pumps, compressors, and human-powered vehicles. Due to their widespread usage, analytical and graphical solutions for the position analysis of slider-crank mechanisms are introduced in various textbooks and lecture notes. Recently, artificial neural network algorithms have been applied to various design applications across different research fields, and mechanism design is no exception. However, investigations into the application of neural network algorithms specifically to slider-crank mechanisms are quite limited. In this study, the analytical position analysis of the slider-crank mechanism is presented, and datasets are obtained first. Subsequently, the application of following three different supervised learning algorithms to the position analysis of the slider-crank (R-RRT) mechanism is investigated using analytical solution datasets: the Levenberg-Marquardt Backpropagation (LM) algorithm, Bayesian Regularization (BR) algorithm, and Scaled Conjugate Gradient Descent (SCG) algorithm. These three algorithms were chosen due to their distinct characteristics. The main objectives of this study are to highlight the differences among various neural network algorithms' applications and to understand the applicability and suitability of different neural network algorithms for kinematic position analysis in mechanisms. The results indicate that the Bayesian regularization algorithm yields the best results, while the Levenberg-Marquardt Backpropagation algorithm exhibits the best performance.