Feed Forward Back Propagation Algorithm Research Articles

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

Synergism of Artificial Intelligence and Techno-Economic for Sustainable Treatment of Methylene Blue Dye-Containing Wastewater by Photocatalysis

Recently, removing dyes from wastewater by photocatalysis has been extensively studied by several researchers. However, there exists a research gap in optimizing the photocatalytic process parameters using artificial intelligence to maintain the associated techno-economic feasibility. Hence, this investigation attempts to optimize the photocatalytic degradation of methylene blue (MB) dye using an artificial neural network (ANN) model to minimize the capital and running costs, which is beneficial for industrial applications. A ZnO/MgO photocatalyst was synthesized, showing an energy band gap of 2.96 eV, crystallinity index of 71.92%, pore volume of 0.529 cm3/g, surface area of 30.536 m2/g, and multiple surface functional groups. An ANN model, with a 4-8-1 topology, trainlm training function, and feed-forward back-propagation algorithm, succeeded in predicting the MB removal efficiency (R2 = 0.946 and mean squared error = 11.2). The ANN-based optimized condition depicted that over 99% of MB could be removed under C0 = 16.42 mg/L, pH = 9.95, and catalyst dosage = 905 mg/L within 174 min. This optimum condition corresponded to a treatment cost of USD 8.52/m3 cheaper than the price estimated from the unoptimized photocatalytic system by ≈7%. The study outputs revealed positive correlations with the sustainable development goals accompanied by pollution reduction, human health protection, and aquatic species conservation.

Development of an energy prediction model for residential buildings using Artificial Neural Network

A model has been developed in this study for predicting the energy consumption of residential building sector using Artificial Neural Network. This model was based on the Multi-Layer Perceptron architecture using feed-forward back propagation algorithm for training. The Levenberg-Marquardt function and the Gradient Descent with momentum function were used as training and learning function, respectively. The mean squared error was used to check the overall performance of the developed model. Trials were performed to finalize the number of hidden layers required for the model. Regression analysis was done between the predicted and actual data to validate the proposed ANN model. The prediction for the same dataset was also performed using the traditional trend extrapolation method, and the predicted results of both were compared with the actual energy consumption data recorded by the electricity regulatory authority of the state. It has been concluded that the accuracy of predicted data using the proposed model was very high (i.e., of 99.54%) as compared to the traditionally used TEM (i.e., 91.07%). MSE achieved for the ANN model was 0.01767% and that of TEM was 0.13115%. The outcome of this study can be used at building level to achieve energy efficiency by predicting the energy consumption and at the level of distribution companies to predict the overall energy demand by the respective sector, and take measures accordingly.

Short-term solar radiation forecasting using machine learning models under different sky conditions: evaluations and comparisons.

Forecasting short-term solar radiation is crucial for many solar energy applications. Additionally, solar energy has a lower environmental impact than conventional sources like fossil fuels and can be used for investment purposes through the construction of large solar farm facilities. To test, evaluate, and compare various solar radiation models, short-term observations of meteorological, astronomical, computational, and geographical data were collected at two distinct locations from 2012 to 2015. In this study, seven machine learning models were employed: multi-layer perceptron (MLP), feedforward backpropagation algorithm (FFBP), autoregressive integrated moving average (ARIMA), linear regression (LR), radial basis function neural network (RBFNN), random forest (RF), and Gaussian process regression (GPR) models. These models were used to forecast hourly global solar radiation (GSR) using the aforementioned data as model input. The performance of the selected models' forecast accuracy was thoroughly examined by assessing it for a typical day, for four seasons, and under three sky conditions. The RF model can forecast GSR with satisfactory accuracy, and MLP and GPR models provide better accuracy than LR, FFBP, RBF, and ARIMA models. For example, the R2 value range of RF are 0.9621 for Tetuan site and 0.9534 for Tangier site, respectively. Meanwhile, RF, MLP, and GPR models under-forecast few high radiation values on clear days, which may due to the differences in training and testing data ranges and distributions of the sky conditions. Finally, the obtained result of this study indicate that the proposed RF model is a reliable alternative for short-term global solar radiation forecasting due to its high forecast accuracy.

Investigation of dual impact of nanoparticles-ethanol as additive to biodiesel-diesel fuel on an engine using artificial neural network prediction model

This paper investigates effects of the dual approach of adding nanoparticles and ethanol to palm biodiesel-diesel fuel at varied percentage as additive. Four samples namely: E10(100) B20, E15(100) B20, E10(100) B50, and E15(100) B50 were investigated on a single cylinder L70N Yanmar engine at constant speed of 2500 rpm under three load categories of 25 %, 50 % and 75 % for each sample. The experimental data was then feed-in as training data to develop an Artificial Neural Network (ANN) using the feed-forward back propagation algorithm (FFBP) and applying the Levenberg-Marquardt training pattern. Four (4) inputs were used viz: engine load, fuel, additive and cetane number. Correlation coefficient (R) and determination coefficient (R2) were used to validate performance of the network with two set of four hidden layers while targeting 5 outputs viz: BTE, HC, CO, NOx, and smoke. Experimental results indicate that at 25 % load the Brake Thermal Efficiency (BTE) values of samples E10(100) B20, E15(100) B20, E10(100) B50, and E15(100) B50 were 10.131 %, 14.972 %, 13.945 %, 14.091 % while at 50 % load it was 18.201 %, 25.101 %, 23.046 %, 23.779 % and at 75 % BTE values was 23.779 %, 32.011 % and 30.826 % which showed engine performance can be improved by magnetite nanoparticles addition with ethanol to biodiesel-diesel fuel up to 7.4 % in terms of BTE compared to only biodiesel-diesel blend fuel; emission was noticeably reduced for tested gases compared to previous literatures. The validated ANN results range 0.99350 to 0.99975 and indicated an acceptable level for the ANN prediction model for BTE. ANN exhaust emission prediction implicated smoke and NOx target performance was most accurate with R and R2 values at 0.9942, 0.9984, and 0.9980, 0.9960 respectively which indicated minimal error and hence the ANN model was satisfactory.

Modelling and Energy Optimization of a Thermal Power Plant Using a Multi-Layer Perception Regression Method

This research paper studies a thermal power plant model with an Artificial Neural Network that contributes to the accuracy improvement of actual measurement data. Neural Networks process the paradigm of algebraic expressions, and their training occurs via a Feed-Forward Back Propagation algorithm implemented in a MATLAB environment. The applied training case in a thermal power plant in Paracha includes three different algorithms, the Levenberg-Marquadt, the Scaled Conjugate Gradient, and the Bayesian Regularization, considering less number of samples to achieve more reliable results. The outcome highlights Bayesian Regularization Networks’ superiority in accuracy and performance compared to Levenberg-Marquadt and the Scaled Conjugate Gradient. The regression analysis estimates the relationship between input-independent and output-dependent variables, forecasts the energetic data, and highlights the benefits of the Bayesian Regularization method in the energy sector.

Modeling of Dry EDM process parameters during machining of Inconel 718 using artificial neural network

Dry EDM is a modified technique with substituted gaseous medium instead of liquid dielectrics in the usual EDM phase. In this work, dry EDM attempt is made with copper electrode on Inconel 718 material using L27 orthogonal array with compressed air as a dielectric medium. This study takes into account process variables like gas dielectric pressure, pulse current, spark on time, and gap spark voltage. ANN models is developed using Feed-forward back propagation algorithms with trainlm, learngdm, MSE, transig as the training, learning, performance and transfer functions respectively for the Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface roughness (Ra) as a response characteristics. A good fitness is observed for the trained model. Experimental response values and the ANN predicted values is compared and found that the overall correlation coefficient was 0.94455 which shows good prediction accuracies and effectiveness of the model and ANN technique.

Performance evaluation of nano refrigerant operated thermosyphon using artificial neural network technique

The present work is focused on evaluating the performance of a thermosyphon with internal fin and internal cut arrangement employing a mixture of nano particle Al2O3 and refrigerant R134a using artificial neural network. Using feed forward back propagation algorithm technique has used the data from the experimental studies to predict the thermosyphon performance. The input parameters to the algorithm were heat input, heat flux and cooling water temperatures and the output parameter predicted by the algorithm is heat transfer coefficient. It was inferred from the analysis that the predicted values of heat transfer coefficient were close to the actual experimental data. The artificial neural network model predicted the values in close agreement with the experimental values with an error of ±3%.

Development of model predictive controller in avoidance design of Unmanned Aerial Vehicle (UAV)

This research was accomplished by employing already developed mathematical model of a 6DoF UAV in free space through the motion of the 6DoF aircraft (unmanned aerial vehicle) determined by coordinate systems which allow an aircraft’s position and orientation in space to be kept tracked. The discrete method of the model developed for simulation in Simulink was realized with a suitable transfer function. Then, an artificial neural network model predictive adaptive controller that can handle the nonlinearities associated with the UAV was developed based on state space technique and the model predictive controller (MPC) utilizes a neural network model to envisage future plant (aircraft) responses to potential control signals. The developed model predictive controller network was successfully trained offline using Feed-forward Back-propagation algorithm with speed and position as inputs since the underlying objective of this work is to improve the speed and position in order to advance the safety collision distance of the UAV because these parameters are mostly considered in the avoidance maneuver performance. Also, the results reveal that the speed of the generated UAV is approximately the input speed of 75MS–1 during the time the UAV is affecting maneuvering. Significance of the result is that the proposed algorithm is capable of generating collision-free trajectory for different waypoint cases.

Enhancing the efficiency of polytetrafluoroethylene-modified silica hydrosols coated solar panels by using artificial neural network and response surface methodology

Abstract In this article, an attempt was made to improve the efficiency of coated solar panels by using artificial neural networks (ANNs) and response surface methodology (RSM). Using the spray coating technique, the glass surface of the photovoltaic solar panel was coated with silicon dioxide nanoparticles incorporated with polytetrafluoroethylene-modified silica sols. Multilayer perceptron with feed-forward back-propagation algorithm was used to develop ANN models for improving the efficiency of the coated solar panels. Out of the 200 sets of data collected, 75% were used for training and 25% were used for testing. On evaluating the models using performance indicators, a four-input technological parameter model (silicon dioxide nanoparticle quantity, coating thickness, surface temperature and solar insolation) with eight neurons in a single hidden layer combination was observed to be the best. The prediction accuracy indicator values of the ANN model were 0.9612 for the coefficient of determination, 0.1971 for the mean absolute percentage error, 0.2317 for the relative root mean square error and 0.00741 for the mean bias error. Using a central composite design model, empirical relationships were developed between input and output responses. The significance of the developed model was ascertained by using analysis of variance, up to a 95% confidence level. For optimization, the RSM was used, and a high efficiency of 17.1% was predicted for the coated solar panel with optimized factors; it was validated to a very high level of predictability. Using interaction and perturbation plots, a ranking of the parameters was done.

Estimation of forced heat convection in a rectangular channel with curved-winglet vortex generator: A machine learning approach

• It was determined that angle of curved winglet, plate temperature, and Re on heat transfer are important parameters. • The findings achieved from the experimental datas have been evaluated as a result of the comparison with the target value within the ±4% diversion interval for all Nu. • The designed artificial neural network ( ANN ) structure indicated optimum mean square error ( MSE ) of 1.4x10 -2 , average relative deviation ( ARD% ) of 0.1771 and correlation coefficient ( R 2 ) of 0.9879 in architecture of average Nu datasets. There are a limited number of studies in the literature on the effect of curved fins on heat transfer. In this study, the effect of novel fin geometry and angle of attack of winglet ( α =30 o , 60 o , and 90 o ) on heat convection is estimated using a machine learning method. Airflow in the rectangular channel is investigated under constant heat flux ( q ''=100 W/m 2 ) and turbulence regime (5683≤Re≤17049) by experimental studies. Improvements in heat transfer are observed at different temperature values (T=30 o C, 50 o C and 70 o C) of the plate on which the blades were attached. In order to investigate the effect of input parameters on the prediction accuracy, an artificial neural network structure consisting of curved fin angle, Reynolds number and heater plate temperature parameters is preferred. Heat transfer is estimated by feedforward backpropagation (FFBP) and multi-layer perceptron (MLP) neural network algorithm using a data set containing 45 empirically obtained values in the forced convection condition. The training network has been calculated through Scaled Conjugate Gradient with ten neurons in the hidden layer. The results obtained from the experimental data assessed in consequence of the comparison with the target value within the ±4% diversion interval for all Nusselt numbers. Optimum mean square error (MSE), average relative deviation (ARD%) and correlation coefficient ( R 2 ) is obtained as 1.4x10 -2 , 0.1,771 and 0.9879 respectively in architecture of average Nusselt number datasets.

Yapay Sinir Ağları (YSA) ve ARIMA Modelleri ile Türkiye’de Aylık Sıfır km Otomobil Satış Adetlerinin Tahmin Edilmesi

Çalışmada, yapay zekâ temelli tahmin modellerinden Yapay Sinir Ağları (YSA) ve Zaman Serileri Analizi modellerinden Arima ile Türkiye’deki aylık sıfır km otomobil satış adetleri tahmin edilmiştir. Bağımlı değişken aylık sıfır km otomobil satışlarıdır. Bağımsız değişkenler ise aylık otomobil ihracatı (USD), aylık otomobil ithalatı (USD), aylık Amerikan Dolar kuru (TL), aylık Türkiye ihracatı (USD) ve aylık Türkiye ithalatı (USD)’dir. Türkiye İstatistik Kurumu (TÜİK), Türkiye Cumhuriyet Merkez Bankasından (TCMB) elde edilen aylık veriler (Ocak 2002-Aralık 2020, 228 ay-19 yıl) kullanılarak, Ocak 2021 ile Mart 2022 arasında gerçekleşen 15 aylık sıfır km otomobil satış adetleri tahmin edilmiştir. YSA modelinin performansı Arima modeli ile karşılaştırılmış, tahminlerin farkları ve sonuçlar yorumlanmıştır. Çalışmada Arima’nın YSA’ya kıyasla daha iyi sonuç verdiği tespit edilmiştir.

Artificial neural network and semi-empirical modeling of industrial-scale Gasoil hydrodesulfurization reactor temperature profile

The sulphur content of the desulfurization reactor product is strongly related to the severity of the reaction, which is determined by the reactor bed temperatures. In this study, the potential of neural network and semiempirical regression models to estimate temperature variation in industrial-scale gasoil HDT reactor was examined to achieve improved prediction ability. The reactor consists of 4 beds with 10 temperature indicators to control and monitor the temperature. Model developing Inlet temperature, H2/HC ratio, LCO flowrate, cracked gasoline flowrate, and straight run gasoil flowrate as input layer parameters and bed section outlet temperature as output layer was used. A database with 283 different data was built using daily records of the HDS process from an Iranian refinery. Feedforward backpropagation algorithm with learngdm learning function was used to develop ANN models. The number of neurons in the hidden layer varied between 7–13 to find the most reliable network. The optimum ANN models were selected for reactor temperature profiles on the trial-and-error method. The performance of designed models was tested by computing root mean square error (RMSE), and average absolute deviation (AAD). Comparing the neural network model and regression models, the artificial neural network model is the best model for the reactor bed temperature prediction. The value of AAD for temperatures sensors 1 to 10 in the artificial neural network model obtained 0.128, 0.094, 0.091, 0.1, 0.032, 0.067, 0.052, 0.031, 0.086, and 0.062 respectively, which show good agreement between the actual data and the artificial neural network predicted data for temperature profile in the length of the reactor.

Determining the position of two cracks in a cantilever beam using artificial neural networks

During functioning time, structures such as beams are subjected to a variety of loads caused by the working conditions and environment, which can lead to the development of cracks. The current research is concerned with detecting the presence and location of two transverse breathing cracks, in cantilever beams. Starting from the known fact that damages produce a stiffness degradation in structures altering their dynamic parameters, we performed modal simulations of damaged beams to determine their natural frequencies. By using the simulation data, we trained an artificial neural network (ANN), using the feedforward backpropagation algorithm, that is capable to detect the presence of the cracks, their position and for the case when the damages are in proximity, the model can determine if the cracks occur on the same face or opposite faces.

Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model

The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).

An Approach to Measure Tool Wear through Image Processing in Incremental Sheet Metal Forming

The Incremental sheet metal forming (ISF) is come into the light due to its unique forming technique. In ISF, the metal sheets are transformed into the final product without using dedicated died. The plastic deformation of metal sheets is conducted through a simple forming tool. Its processing time resembles that the ISF is suitable for the formation of customized products, prototypes, and low volume production. Tool life in manufacturing processes is an important consideration for productivity. The present study is an approach to use image processing techniques to measure the exact location and amount of tool wear in the ISF tool which is made of 440C steel. Presently the complex histogram plot is under study. Therefore to predict the tool wear, feed-forward backpropagation (FFB) algorithm is utilized. It is reported that the maximum predicted tool wear of 0.0663mm is found in the trail run 05 with an error of 0.0104mm. The best-fitted value of the FFB model is observed at the epoch 05 with the value of 5.922e-005. The overall coefficient of performance i.e. R2 of FFB modeling is reported as 90.52 % with the mean absolute error (MAE) of 0.0042 which shows a good agreement of the prediction model.

Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

AbstractFused deposition modeling (FDM) is one of the most economical and popular technology amongst numerous additive manufacturing techniques. The quality of FDM fabricated parts is highly sensitive to the production parameters. Thus, in the present work, an investigation on the FDM printed polylactic acid parts has been performed considering six printing process parameters, that is, nozzle diameter, build orientation, raster pattern, layer height and print speed to develop the feedforward backpropagation (FFBP) artificial neural network prediction model for the prediction of responses, namely, tensile strength, material consumption, build time and surface quality. Tensile specimens as per L27 orthogonal array are printed considering the various combination of parameters. The printed samples have been subjected to tensile strength testing, surface roughness measurement, build time recording, and material consumption evaluation. The highest tensile strength of 57.633 MPa, lowest surface roughness of 1.71 μm, lowest build time of 0.35 h and lowest material consumption of 7.8 g are observed. The experimental results have been used to develop the artificial intelligence‐based prediction model through FFBP algorithm and sigmoid transfer function to predict the responses. The best performance of the developed neural network with R2 for testing (0.99343), training (0.99366), and validation (0.99372) of data is recorded for prediction of responses with minimum percentage error. The study concluded that developed model is capable of predicting the responses of FDM process according to the input process parameters.

An Empirical Study of Passengers’ Perceived Satisfaction with Monorail Service Quality: Case of Kuala Lumpur, Malaysia

The aim of the current study was to examine passengers’ perceived satisfaction with a monorail system by determining the factors that dominated their perception. For this study, 417 data points were collected through face-to-face questionnaire surveys in Kuala Lumpur, Malaysia, from 20 September 2019 to 10 December 2019. The study involved the use of several assessments to ascertain how the perceived satisfaction of passengers with the monorail service was influenced. The tests predicted which service factors fuelled this satisfaction and included an exploratory factor analysis, Spearman’s correlation test and an artificial neural network (ANN) model (termed the multilayer perceptron neural networks model) with a feed-forward backpropagation algorithm. The findings that were produced by these methods of analysing factors revealed the extraction of eight service-quality features as the key influences on the perceived satisfaction of passengers. The correlation test results revealed that these factors have a significant and positive relationship with perceived satisfaction. Finally, the ANN model with the optimum neuron number in the hidden layer is seven neurons. This model found that the dominant service quality factors that contributed to influencing passengers’ perceived satisfaction levels were (i) the provision of information, (ii) facilities, (iii) signage. The results of this study will benefit service providers, policymakers and planners in formulating effective strategies to enhance passengers’ satisfaction with the monorail service and increase the ridership.

Development of artificial neural network to predict the performance of spark ignition engine fuelled with waste pomegranate ethanol blends

In this study, an artificial neural network (ANN) is developed to predict the performance of a spark-ignition engine using waste pomegranate ethanol blends. A series of experiments on a single-cylinder, four-stroke spark-ignition engine yielded the data needed for neural network training and validation. 70 percent of the experimental data was used to train the network using the feed-forward back propagation (FFBP) algorithm. The developed network model's performance was evaluated by contrasting its output with experimental results. Input parameters included engine speed, ethanol blends, and output parameters included indicated and brake power, thermal, volumetric, and mechanical efficiencies. Training and testing data had regression coefficients that were almost identical to one. The research revealed that the ANN model can be a better option for predicting engine performance with a higher level of accuracy.

ESTIMATION OF KNEE JOINT TORQUE DURING SIT–STAND MOVEMENT BASED ON sEMG SIGNALS USING NEURAL NETWORKS

The estimation of knee joint torque is important for the development of powered exoskeletons to achieve ideal gait characteristics. In this study, we proposed three different models to predict the required torque for performing sit-to-stand (STS) and back-to-sit (BTS) movements. The surface electromyography (sEMG) signals were extracted from the biceps femoris and rectus femoris muscles during STS and BTS movements. The time-domain features selected as input to the models for torque prediction are integrated EMG (iEMG), root mean square (RMS), and mean absolute value (MAV). Two-way ANOVA analysis identifies the significance of NN models and EMG features of the muscles in predicting the knee joint torque requirement. The artificial neural network models selected for prediction are the feed-forward back-propagation algorithm, ANFIS, and NARX. The theoretical value of knee joint torque calculated using the Lagrange method was compared with the torque output for each model based on root mean square error (RMSE). The desired torque predicted using the NARX model confirms to have the least average error ([Formula: see text][Formula: see text]Nm), which indicates that NARX can estimate knee joint torque more accurately from sEMG than other models.