Artificial Neural Network System Research Articles

The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.

Multi-objective optimization and performance assessment of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy interfence system (ANFIS) for estimation of fouling in phosphoric acid/steam heat exchanger

Fouling is a common occurrence in industrial heat exchangers, leading to a decrease of the thermal efficiency. This research addresses the challenge of applying various machine learning algorithms including Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to effectively model the fouling resistance in a heat exchanger in phosphoric acid concentration loop. Subsequently, a multi-objective optimization approach was employed to minimize the fouling resistance. Confirmatory experiments are then conducted in the phosphoric acid concentration plant using optimized variables.The findings of this study indicate that the three models accurately align with one year of operational data, achieving a remarkably high coefficient of correlation (R). Among the models utilized, RSM demonstrates the highest level of prediction accuracy, with an R of 0.9998, accompanied by the lowest mean square error (MSE) of 5.9388 10−13 and root mean squared error (RMSE) of 7.7064 10−7. The RSM optimization process identifies the optimal conditions for variables such as time, acid inlet and outlet temperature, steam temperature, acid density, and volume flow rate, which are determined to be 114.805 h; 72.214 °C; 80.407 °C; 116.784 °C; 1642.47 kg/m3; and 2308.1 m3/h, respectively. The predicted fouling resistance demonstrates a strong correlation with the actual data, with a negligible percentage difference of 2.19 %, further validating the accuracy and reliability of the RSM model.

Predicting the precipitated calcium carbonate and unconfined compressive strength of bio-mediated sands through robust hybrid optimization algorithms

Biological soil stabilization through Microbial-Induced Calcite Precipitation (MICP) demonstrates the potential to be an eco-friendly and sustainable approach to enhance the strength properties of cohesionless granular soils. No systematic study has been performed so far in the literature on predicting the strength of bio-mediated sands prior to testing. In this study, to overcome this deficiency, based on a comprehensive set of experimental data compiled from literature, the amounts of Precipitated Calcium Carbonate (PCC) and Unconfined Compressive Strength (UCS) of MICP-treated sands are estimated through hybridizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models with metaheuristic algorithms. During the data extraction procedure, a total of 70 and 115 datasets are used to predict the PCC and UCS, respectively. To this end, five key features of urea concentration (Mu), calcium chloride (Mcc), optical density of bacterial suspension (OD600), pH and total volume of injection per volume of sample (Vt), are first selected as the inputs of the hybrid models to estimate the amount of calcium carbonate precipitation within the porous structure of MICP-treated granular medium. The experimental data on the PCC along with the basic characteristics of the host sand including mean grain size (D50), uniformity coefficient (Cu) and void ratio (e), are then adopted as the inputs of the numerical models in the second phase whilst the UCS of bio-mediated sands is set as the output. The performance and accuracy of implemented models are rigorously assessed through analyzing various statistical indices and performance criteria. According to the numerical analysis, the ANN-Particle Swarm Optimization (PSO) and ANFIS-PSO hybrid models show the best performance in predicting PCC and UCS of bio-mediated sands, respectively. Using the well-established Gene Expression Programming (GEP) algorithm, practical correlations are developed to predict the PCC and UCS values of MICP-mediated soils based on the characteristics of biological binding agents as well as the host material. The Machine Learning (ML)-based models presented in this study provide engineers with a fruitful framework for the rough and preliminary estimation of the amount of calcite precipitation as well as the UCS of bio-mediated sands in the field prior to stabilization and performing complementary tests. Nevertheless, it is important to consider the limitations of extrapolation in the models presented in this research.

Modelling and optimizing the transesterification process of shea butter via CD-BaCl-IL catalyst using soft computing algorithms

Shortage and environmental threats posed by fossil fuel have become a critical issue requiring searching for alternative energy sources. This study utilized clay-doped barium chloride and ionic liquid (CD-BaCl-IL) as a catalyst for optimizing biodiesel production from shea butter. 10% barium chloride and ionic liquid were blended with clay, dried, and then calcined for 4 h at 600 °C to develop the catalyst. The synthesized catalyst and the biodiesel were suitably characterized. Response surface methodology (RSM) implementing Central Composite Design (CCD) and Genetic Algorithm (GA) were employed to model and optimize the effect of the process parameters on the response. The model's capabilities were evaluated using coefficient of determination (R2) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R2 -0.9945, Adjusted R2-0.9846, Predicted R2-0.8694) demonstrating the model's acceptance. Artificial Neural Network (ANN) and Adaptive Neurofuzzy Inference System (ANFIS) were used to assess the model's capability. The obtained statistical results - (R2 = 0.8694, MSE = 0.7035), ANN (R2 = 0.999, MSE = 0.0001026), ANFIS (R2 = 0.99, MSE = 0.000041), showed that ANFIS had the best prediction with the lowest MSE. ANFIS integrated with GA (GA-ANFIS) gave the best optimization with a biodiesel yield (96.72%) at a catalyst concentration of 4 wt%, a methanol/mol ratio of 10:1, a time of 2.5 h, a temperature of 70 °C, and an agitation speed of 400 rpm. The optimal developed biodiesel properties were successfully evaluated within the ASTM D 6751 standards.

Pseudograin decomposition of short fiber-reinforced plastics for two-step homogenization using machine learning approach

Decomposing the representative volume element (RVE) of short fiber-reinforced plastics (SFRPs) into several pseudograins (PGs) is essential for understanding its effective mechanical behavior. However, conventional PG decomposition methodologies are limited by their high computational costs due to iteration-based algorithms. To address this, we propose a machine learning-assisted PG decomposition procedure that utilizes a series–parallel artificial neural network (ANN) system to facilitate the time-consuming decomposition process. To validate the effectiveness of our proposal, we implemented a two-step homogenization framework of SFRP that consists of the series–parallel ANN system, Mori-Tanaka model, and Voigt model into ABAQUS user material subroutine (UMAT). The elastic modulus values predicted by the UMAT are found to be in good agreement with both DIGIMAT-MF and experimental values, while also maintaining low computational time.

Crime Prevention Through Environmental Design: Numerical investigation of environmental factors affecting security in sports facilities

Sports are one of the most essential categories of developed and developing societies, among which football, as the most popular sport in the world, receives special attention from governments. Therefore, to increase the attendance of people in stadiums, it is necessary for the managers to identify the factors influencing the attendance of spectators, both psychological and internal factors, as well as external and environmental factors, and to design and implement a written and specific strategy and plan. Having a sense of security is one of the influential factors in creating satisfaction in the audience. Due to the high cost of building sports facilities and spaces, care should be taken about many factors and variables, especially the correct location of such spaces, to enable easier access, fairer distribution, and greater security. Therefore, this research aims to analyze the environmental factors that affect security in sports facilities with the approach of crime prevention through environmental design in stadiums. The instrument in this research is a questionnaire on security in sports facilities with the effectiveness of crime prevention through environmental design. Finally, the artificial neural network system method was used in MATLAB (A 2018) and SPSS (V24) software to analyze the data and answer the research questions. Environmental factors of access, light and illumination, readability, visibility and natural surveillance, repair and maintenance, dynamics, functional disorders, and guide signs respectively had a positive effect on security and crime prevention through environmental design in sports facilities. Therefore, sports directors and architects must pay attention to these indicators in the design of sports stadiums. Keywords: Stadiums, Sport Management, Sports Events, CPTED Approach, Neural Networks.

Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing.

Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.

87P An artificial neural network system to predict the response to sintilimab based on the RNA data of ORIENT-3 study

87P An artificial neural network system to predict the response to sintilimab based on the RNA data of ORIENT-3 study

Optimization of waste tyre steel fiber and rubber added foam concretes using Taguchi method and artificial neural networks

This research presents a study on the waste tyre steel (WTS) and rubber particles (RP) added foam concrete using Taguchi method and Artificial neural networks (ANN) by revealing its potential utilization in construction industry. The fresh and hardened characteristics of foam concretes were experimentally tested. Twenty-five different samples were first prepared according to the Taguchi design and compressive and flexural strengths, dry density and thermal conductivity of the samples were optimized. A Quasi-Newton based ANN algorithm was proposed to obtain prediction equations for the flexural and compressive strength of foam concretes. ANOVA analysis was also conducted to determine the most important factors. The results showed that the combined utilization of WTS and RP decreases the harmful effect of RP on the strength properties of foam concretes. Taguchi optimization results were confirmed through validation tests. With the proposed ANN method, R2 values were obtained as 0.9775 for compressive strength, 0.9723 for flexural strength, 0.9654 for dry density and 0.9527 for the thermal conductivity measurements. Both Taguchi and ANN system were found to be suitable for the design and estimation of the responses.

Insights into the prediction of the liquid density of refrigerant systems by artificial intelligent approaches

This study presents a novel model for accurately estimating the densities of 48 refrigerant systems, categorized into five groups: Hydrofluoroethers (HFEs), Hydrochlorofluorocarbons (HCFCs), Perfluoroalkylalkanes (PFAAs), Hydrofluorocarbons (HFCs), and Perfluoroalkanes (PFAs). Input variables, including pressure, temperature, molecular weight, and structural groups, were systematically considered. The study explores the efficacy of both the multilayer perceptron artificial neural network (MLP-ANN) and adaptive neuro-fuzzy inference system (ANFIS) methodologies in constructing a precise model. Utilizing a comprehensive dataset of 3825 liquid density measurements and outlier analysis, the models achieved R2 and MSE values of 0.975 & 0.5575 and 0.967 & 0.7337 for MLP-ANN and ANFIS, respectively, highlighting their remarkable predictive performance. In conclusion, the ANFIS model is proposed as an effective tool for estimating refrigerant system densities, particularly advantageous in scenarios where experimental measurements are resource-intensive or sophisticated analysis is required.

Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module

In this work, an Artificial Neural Network (ANN) with a backward-propagation technique was used to predict the power generation of the Photovoltaic (PV) module in weather conditions of Baghdad city-Iraq. Experiment tests were investigated in the summer of 2022. Three weather parameters, including: (solar radiation, ambient temperature, and wind speed), the output electrical characteristics of the PV module (voltage, current, power), and module temperature (were measured. Therefore, the dataset of the ANN system consists of four input and one output parameter. Furthermore, the structure of ANN includes a single hidden layer with a backward propagation technique. The main goal of this study was to optimize the number of neurons in the training process. The evaluation of the ANN model depended on the determination coefficient (R) and Root Mean Squared Error (RMSE).  The obtained results show that the architecture of ANN is appropriate for predicting the power generated from the PV module. The developed ANN model has good accuracy. Where the MSE is 0.002747 at epoch 9 in the model. Furthermore, the R is recorded as 0.99078, 0.98254, 0.99125, and 0.99005 for training, testing, validation, and all respectively in the proposed model. In addition, the optimization number of neurons in the hidden layer gave sufficient accuracy without referring to the choice of the number of neurons by using the trial-and-error method that most researchers relied.

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study; the particle swarm optimization (PSO) algorithm was also used to optimize the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analyzed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively.

Machine learning for power transformer SFRA based fault detection

This paper presents machine learning methods for health assessment of power transformer based on sweep frequency response analysis. The paper presents an overview of monitoring and diagnostics based on statistical Sweep Frequency Response Analysis (SFRA) based indicators that are used to evaluate the state of the power transformer. Experimental data obtained from power transformers with internal short-circuit faults is used as a database for applying machine learning. Machine learning is implemented to achieve more precise asset management and condition-based maintenance. Unsupervised machine learning was applied through the k-means cluster method for classifying and dividing the examined power transformer state into groups with similar state and probability of failure. Artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) as part of supervised machine learning are created in order to detect fault severity in tested power transformers of different lifetime. The presented machine learning methods can be used to improve health assessment of power transformers.

Modeling the effects of essential heavy metals on environmental pollution: A linear and nonlinear prediction model via cascade forward‐neural network

The use of artificial neural networks (ANN) in studies with different content is increasing day by day, and its impact and acceptability are also increasing. The importance of using ANN in popular and large‐scale fields such as environmental pollution studies is known. Studies are carried out considering the need to develop ANNs, which can be an alternative to the data obtained as a result of the analyses made with instrumental devices that require high costs and trained human labor. This study, which was carried out using real data and samples showing biomonitoring properties such as soil and plants, is an example to fill the gaps in the literature. In this study, tools that can model both linear and nonlinear relationships were used. The simulation of the dynamic ecological system containing Fe, Mn, and Ni heavy metals was carried out from the point of view of artificial intelligence. It is revealed that ANN systems are supportive methods in studies of determining environmental pollution, especially in biomonitoring studies.

Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass

The adsorption properties of 2,4-Dichlorophenoxyacetic acid (2,4-D) onto biochar, obtained through HCl-assisted hydrothermal carbonization process of Vateria indica fruits (VI-BC), were extensively studied using traditional and statistical physics approaches. The traditional adsorption investigations encompassed kinetics, equilibrium, and thermodynamics studies. Subsequently, the Hill statistical physics model was employed to interpret the mechanism. Also, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) machine learning tools were successfully employed to model the adsorption data wherein both models had high prediction potential (R2 > 0.99). The outcomes demonstrated that the produced VI-BC exhibited remarkable adsorptive traits, having a considerable specific surface area (111.54 m2/g), pore size (5.89 nm), a variety of functional groups, and appropriate attributes for efficiently adsorbing 2,4-D. For 10 mg/L 2,4-D, at pH 2.0 and with 0.3 g/L dose, an impressive 91.67% adsorption efficiency was achieved within a 120-min. Pseudo-second-order model aptly depicted the kinetic behavior of 2,4-D adsorption, while the Freundlich model provided a more accurate representation of the isotherms. 2,4-D maximum adsorption capacity stood at 131.39 mg/g at 303 K. The Hill statistical physics model elucidated that the adsorption primarily occurred via physisorption mechanisms, involving electrostatic attractions, π-π conjugation, and pore filling. This conclusion was further substantiated by post-adsorption characterization of the VI-BC. Thermodynamic analysis indicated that the interactions between VI-BC and 2,4-D were favorable, spontaneous, and exothermic. The calculated low energy of adsorption (1.255 kJ/mol) and ΔH° value (−20.49 kJ/mol) further supported physisorption as the dominant mechanism. In summary, this study underscores the significant potential of the newly developed biochar as a promising alternative material for efficiently removing the 2,4-D herbicide from polluted environments.

A novel approach for precipitation modeling using artificial intelligence-based ensemble models

Precipitation is the primary component of the hydrologic water cycle and its accurate prediction plays a significant role for the planning, management and design of hydraulic structures. The objective of the study intended to explore a new approach to increase prediction efficiency of precipitation in the arid, semiarid and humid zones. The approach was implemented using relevant data from stations in Iraq and Nigeria. Support vector regression (SVR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system models (ANFIS) were applied for a single modeling of 30 years monthly average precipitation data. Thereafter, a nonlinear ensemble and 2 linear ensemble techniques were applied to improve prediction efficiency and reliability of the single models. Error measures as well as a goodness of fit measure were the criteria employed to assess the performance potential of the models. Based on the results of this work it was found that ensemble modeling could improve prediction accuracy of the single models as much as 38% in the validation phase. It was also confirmed that the performance of artificial intelligence-based models can efficiently be improved by the application of ensemble modeling in the arid, semiarid and humid study stations of Iraq and Nigeria.

Enhanced Daily Global Solar Radiation Prediction Through Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System with Meta-Heuristic Algorithm Integration

Enhanced Daily Global Solar Radiation Prediction Through Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System with Meta-Heuristic Algorithm Integration

A neural network for monitoring and characterization of buildings

This paper continues work from part 1 where a high precision estimator for energy efficiency and indoor environment based on artificial neural networks (ANN) was examined. Part 1 demonstrated that creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. Now, we extend our requirements for the model to be rapid and precise. At the end of this work we obtain a road map for the design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. This paper also shows that ANN system designed may have a high precision in characterizing the response of the building exposed to variable outdoor climatic conditions. The absolute value of the relative errors, MaxAR, is less than 2%. It proves that monitoring and ANN-based characterization approach can be used for different buildings, including those with the best environmental performance.

Micro-ECDM Performances Analysis Using Fuzzy Logic and ANFIS During Micro-Channel Fabrication on Silica Glass

Higher accuracy and meticulousness are highly demandable in modern industrial field during micro-machining performances by electrochemical discharge machining (ECDM) process. The paper deals with the experimental fuzzy logic control (FLC) analysis as well as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) analysis during micro-channel fabrication on silica glass. A comparative analysis of FLC, ANN as well as ANFIS has been performed and experimental error prediction has been propounded to estimate minimum error possibilities such as mean absolute error (MAE), root mean square error (RMSE) and regression value (R). Computational time training dataset, minimum sample size and prediction time are also illustrated in this paper. In this paper, ANN, FLC and ANFIS models are analyzed for tool wear rate (TWR) and heat-affected zone (HAZ). 3D Rule Viewer and regression analysis as well as validation of test results and characteristics graph between performances with number of epochs of ANN model also are included in this paper for TWR and HAZ. Influence of process parameters like voltage, duty ratio, pulse frequency and electrolyte concentration on Surface Viewer of TWR and HAZ is also illustrated. It is found that ANFIS has great effectiveness of prediction of error during micro-ECDM process.

A Hybrid Optimization-Based Artificial Neural Network Model for Wireless Power Transfer in Electric Vehicles

Electric Vehicles (EVs) are powered by a battery mounted in the vehicle, which powers the motor and drives the wheels. Most commercial EVs can be charged by plugging them into a charging station. Such conductive recharging has various drawbacks, including physical plugging of the cable, safety concerns, and charging time. Manually charging EVs might be dangerous due to the chance of an electric spark or disaster. Advances in Wireless Power Transfer (WPT) demonstrate the capacity to transfer significant amounts of electricity over short and medium-range distances. The ultimate purpose of this paper is to improve the efficacy of electric car wireless charging systems. Here, a hybrid optimization-based Artificial Neural Network (ANN) model is applied to improve the efficacy of the WPT model in EVs. To optimize the weights of the ANN classifier, a hybrid approach termed as Grasshopper-Assisted Elephant Herd Optimization (GA-EHO) method is proposed. The GA-EHO is derived through the hybridization of Elephant Herd Optimization (EHO) Algorithm and the Grasshopper Optimization Algorithm (GOA) techniques. Finally, the experimental study reveals that at 70% learning rate, the proposed ANN system achieves a minimal MSE value of 0.0528, which is lower than other current classifiers, such as SVM, LSTM, and CNN.