Artificial Neural Network Methodology for Three-Dimensional Seismic Parameters Attenuation Analysis

This research investigated and optimized the separation of carbon dioxide (CO2) from natural gas in an adsorption column filled with grafted-beam nanofiber adsorbent. The main purpose of using ANN and RSM models in this manuscript is to compare these two methods in predicting the CO2 adsorption capacity. In other words, it was made to find a suitable model that has the highest agreement with the experimental data. Also, the other purpose of using the RSM model is to detect the optimized empirical conditions. Moreover, two common ANN models are applied in this work, including a multilayer perceptron (MLP) and radial basis function (RBF). The novelties of this work are explained as follows: (1) detecting the optimized synthesis factors of NF-PAN/PUGMA sorbent which possess the highest CO2 adsorption capacity with the help of response surface methodology (RSM), (2) studying the simultaneous interaction of synthesis parameters on the CO2 adsorption capacity with the help of both RSM and artificial neural networks (ANNs), (3) testing two types of ANN models including multilayer perceptron (MLP) and radial basis function (RBF) to predict the effect of monomer volume percentage and irradiation dose on the CO2 adsorption capacity. Indeed, finding the best model (ANN or RSM) can help engineers in practical applications predict CO2 adsorption capacity using NF-PAN/PUGMA under different conditions without incurring high-priced chemical materials, electricity, or human resources. The validation results were examined using correlation coefficients (R2) of RSM, RBF, and MLP models. The correlation coefficients for the RSM, RBF, and MLP models were 0.9910, 0.9949, and 0.9968, respectively. Additionally, the average absolute relative deviation (AARD) values for the RBF and MLP models were 0.00046512 and 0.00045511, respectively, indicating that the MLP model is better than the RBF model. To identify the optimal network structure, the trial-and-error method was conducted for MLP and RBF models. The number of neurons was found at 12 and 45 for MLP and RBF, respectively. The optimized effective parameters were obtained using RSM: 25.80% GMA, 66.45% amine, and an irradiation intensity of 28kGy.

In this work, the right training criterion for optimal prediction of the desired output by the neural network model was our focus. The application of the early stopping technique in the neural network training of radial basis function model in comparison to the same model neural network training without application of early stopping technique was the basis for the work and the work validates the importance of early stopping technique for good network generalization through prevention of overfitting. An over-fitted radial basis function neural network model possesses the tendency for dataset memorization including noise and thus, poorly learns the dataset pattern. From the training results, the application of an early stopping technique inclusive of other vital hyper-parameters during the training of the radial basis function model demonstrates a measure that is very imperative in the prevention of over-fitting during network training. The training results show that the radial basis function model training without the application of an early stopping technique displayed a “learning and slow” phenomenon where the model stopped learning the dataset at a stage without optimum prediction of the dataset pattern. This phenomenon was evaded by the application of an early stopping technique at the ratio of 70%:15%:15% for training, testing, and validation of the standardized dataset. The radial basis function model neural network training regression results show optimum prediction results of R=0.99420 with a training set of R=0.9985 and a test set of R=0.9899 showing very close prediction of the training set by the test set on training with early stopping technique in comparison to the model training without the application of early stopping technique where R= 0.97200 with a training set of R= 0.9840 and a test set of R=0.9612 showing very wide prediction of the training set by the test set. Overfitting often results in good model training but poor test error as the network model learns the wrong structure in the training data or stops learning at a point as seen from the work. This was optimally evaded on the application of the early stopping technique in the work for robust prediction of signal power loss using a long-term evolution dataset.

In this paper, based on mm-wave 60 GHz channel measurements performed in large hall and corridor for both LoS and NLoS scenarios, channel fading effects like received power, path loss and shadowing are investigated based on radial basis function (RBF) neural network model. Results show that RBF model can fit measurement data better than traditional back propagation (BP) machine learning (ML) method with larger coefficient of determination and smaller root mean square error (RMSE). Neural network models can accurately predict channel parameters, indicates the advances of ML in channel modeling. The presented results are useful in design of 5G wireless communication systems and system development.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

Water temperature is considered as one of the most important parameters which influence the growth rate and development of sea cucumbers as well as their distribution within the pond environment. As the change process of water temperature is dependent on the complicated meteorological and geophysical conditions, artificial neural network with specific features such as non-linearity, adaptivity, generalization, and model independence will be a proper method for solving this problem. This paper presents a Radial Basis Function (RBF) neural network model based on nearest neighbor clustering algorithm and puts forward some improved methods aiming at looking for the defects of original algorithm, then integrated them into an optimization model and verified it on matlab platform. Finally, a comparison between RBF model and 1-D vertical model was made to confirm the excellent predictive performance of optimized RBF neural network model.

A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms

Inrecent years, artificial neural networks have become the subject ofintensive research inanumber ofscientific areas. The high performance and operational speed ofneural models open up awide spectrum ofapplications invarious areas oflife sciences. Objectives pursued bymany scientists, who use neural modelling intheir research, focus - among others - onintensifying real-time calculations. This study shows the possibility ofusing Multilayer-Perceptron (MLP) and Radial Basis Function (RBF) models ofartificial neural networks for the future development ofnew methods for animal science. The process should beexplained explicitly tomake the MLP and RBF models more readily accepted bymore researchers. This study describes and recommends certain models aswell asuses forecasting methods, which are represented bythe chosen neural network topologies, inparticular MLP and RBF models for more successful operations inthe field ofanimals sciences.

Accurate measurements of PM2.5 concentration over time and space are especially critical for reducing adverse health outcomes. However, sparsely stationary monitoring sites considerably hinder the ability to effectively characterize observed concentrations. Utilizing data on meteorological and land-related factors, this study introduces a radial basis function (RBF) neural network method for estimating PM2.5 concentrations based on sparse observed inputs. The state of Texas in the USA was selected as the study area. Performance of the RBF models was evaluated by statistic indices including mean square error, mean absolute error, mean relative deviation, and the correlation coefficient. Results show that the annual PM2.5 concentrations estimated by the RBF models with meteorological factors and/or land-related factors were markedly closer to the observed concentrations. RBF models with combined meteorological and land-related factors achieved best performance relative to ones with either type of these factors only. It can be concluded that meteorological factors and land-related factors are useful for articulating the variation of PM2.5 concentration in a given study area. With these covariate factors, the RBF neural network can effectively estimate PM2.5 concentrations with acceptable accuracy under the condition of sparse monitoring stations. The improved accuracy of air concentration estimation would greatly benefit epidemiological and environmental studies in characterizing local air pollution and in helping reduce population exposures for areas with limited availability of air quality data.

Efficient global optimization of aerodynamic shapes with the high-fidelity method is of great importance in the design process of modern aircrafts. In this study, modifications are made to the particle swarm optimization (PSO) algorithm and the radial basis function (RBF) model for further improvements of efficiency and accuracy of optimization. Specifically, a PSO algorithm with randomly distributed cognitive and social parameters, exponential decrease of maximum velocity and inertia weight, and periodic mutation of particle position is proposed. Furthermore, a nonuniform shape parameters strategy is introduced for the RBF surrogate model. Validations on test functions show that the new PSO has remarkable speed of convergence, and the new RBF model has superior approximation accuracy. The PSO algorithm and RBF model are then combined to construct the computational fluid dynamics (CFD)–based optimization framework. Finally, optimizations of transonic airfoil and supersonic launch vehicle are perfo...

SUMMARY & CONCLUSIONSThe objective of this research is to assess the reliability characteristics of the Allison T-56 aircraft turbine system which is subjected to local GCC environment where sand erosion is a major factor in reducing turbine life. To utilize this study, mor than thirty years of local fielded data were used for failure rate prediction and validation. The work utilizes Artificial Neural Networks (ANN) employing feed-forward Back-Propagation (BP) and Radial Basis Functions (RBF) neural network algorithm to predict the general T-56 engine turbine failure rate and the failures which required an overhaul maintenance, The results show that the failure times predicted by the RBF model. is closer in agreement with actual failure date than BP model. By the end of the study, we forecast the general failure rate of T-56 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using Multilayer Perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer.

Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

Mercury is a highly poisonous metal which is mostly found in environment. It is considered at the top of the parameters of water quality that requires investigations for planning and management. To understand the status of mercury in the groundwater of Naameh Landfill, Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Mercury. Two types of feed forward networks have been used including multilayer perceptron (MLP) and radial basis function (RBF). A number of different MLP neural networks algorithms and RBF networks trained and developed with reference to pH, EC, TDS, TON, calcium and magnesium to predict Mercury concentration in groundwater. Six scenarios were used to train MLP and RBF networks for choosing the best-fit model for predicting water quality parameters in groundwater of Naameh Landfill. The performances of MLP and RBF models were evaluated by utilizing the coefficient of determination (R2). The results showed that the computed values of R2 for MLP and RBF were 0.791 and 0.881respectively. In addition, the prediction results showed that both types of networks are very good for predicting Mercury concentration in the ground water of our study area. Moreover, the results showed that there are mercury residues for 2 years ahead even if there is no discharge in this place. As a matter of fact, there are no studies that encompass status of heavy metals in municipal solid waste landfills in Lebanon or neighboring countries using ANN models. Thus, this study can be described as unique as it demonstrated a 9 year groundwater data (2011-2019), presented data and projected data for two upcoming years. This is crucial especially in the continual waste crisis that Lebanon is facing and the absence of sustainable disposal practices. This data is a rigid base and a solid reference for developing adequate solutions to prevent future contamination of groundwater with its associated negative impacts on the health and wellbeing of individuals.

To ensure the control of the precision of air-fuel ratio (AFR) of port fuel injection (PFI) spark ignition (SI) engines, a chaos radial basis function (RBF) neural network is used to predict the air intake flow of the engine. The data of air intake flow is proved to be multidimensionally nonlinear and chaotic. The RBF neural network is used to train the reconstructed phase space of the data. The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer. The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model. The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48, respectively.

In this paper, a time-varying channel model is proposed according to radial basis function (RBF) artificial neural networks (ANN). The channel models including path loss plus shadow fading, number of paths, direction-of-arrival (DoA), etc are developed by RBF based on channel measurements performed in mm-wave 26 GHz band in an outdoor microcell. Results show that the RBF models can accurately playback the measured data, and the parameters extracted from the RBF model can be used in time-varying channel simulation practically. Therefore, the model is expected to overcome the shortcomings in existing statistical modeling approach and match with real measured environment, which is useful for 5G system and link level simulations.