Simulation of an artificial neural network for predicting temperature and cutting force during grinding using CAMQL

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination (<TEX>$R^2$</TEX>) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca2+), magnesium ions (Mg2+), sodium ions (Na+), chloride ions (Cl−), sulfate ( SO 42−), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R2) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R2 between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.

Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.

Fly ash and phosphogypsum are industrial by-products requiring a cost to get rid of. Their potential use in the synthesis of geopolymer bricks provides great benefits such as the saving use of natural resources and the solid by-product waste management. Compressive strength is the most important parameter for geopolymer bricks design. In this study, two artificial neural networks, namely the multilayer perceptron (MLP) and the radial basis function (RBF) networks, have been investigated to predict the compressive strength. While developing the MLP or RBF models, 99 experimental observations were used for training and testing. Two evaluation steps were performed: The first step determined the effective number of hidden layers and neurons in each hidden layer as well as the appropriate activation function in predicting the compressive strength. The second evaluation step evaluated the accuracy with which the model would predict the compressive strength of geopolymers. The MLP neural network with two hidden layers having 8 and 10 neurons and the hyperbolic tangent activation function was the best model for predicting the compressive strength. Artificial neural networks can be used as a reliable and accurate technique for estimating the parameters of geopolymer materials.

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.

This article explores the use of single trial EEG signals to predict the voluntary movements of single hand and two hands. During single-hand movements, three kinds of task, grasping, releasing, and holding were considered. The tasks considered during two-hand movements are left and right grasping, left and right releasing, and holding. The subject performs the tasks spontaneously without waiting for and responding to any external cues. In addition, a neural adaptive noise canceller is developed that accomplishes eye blinks suppression. The neural adaptive filter is here implemented by means of a three-layer feed-forward neural network. The feature vectors are formed from the three channels (Fz, C3, and F3). We employ the multilayer perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) network with stochastic gradient learning rule for discriminating different patterns of the EEG signals. In the classical approach to RBF and MLP network implementation, the number of hidden units is predetermined. It, usually, results in too many hidden units. To overcome this drawback, we develop an enhanced resource-allocating network (RAN) for discriminating the EEG patterns. These networks start with no hidden units and grow by allocating new hidden units based on the novelty in the EEG signals, which arrive sequentially. The results of this analysis show that the neural networks would be able to detect the movements of a single hand and two hands with an average classification accuracy of 98.82% and 96.40%, respectively. Moreover, the RAN provides a reduction in the training epochs as compared to the MLP and RBF networks. This work represents a promising approach to control prosthesis device.

A hybrid neural network architecture is investigated for modeling purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers, the first hidden layer is selected to be an adaptive reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid can provide significant advantages over standard MLPs and RBFs in terms of fast and efficient learning, and compact network structure.

For evaluating the accuracy and capability of MLP (Multi Layer Perceptron) and RBF (Radial Basis Function) classifier algorithms; projection profile features for the character images are extracted and are merged with the binarization features after preprocessing every character image, and the combined features thus obtained are used to train both the classifiers (MLP and RBF Network) selected for performing character recognition task. Simulation studies are examined extensively and the proposed hybrid features are found to deliver the recognition accuracy of 93.46% when used with RBF Network as a classifier which is better than the 88.76% accuracy obtained when used with MLP Network as a classifier.

We aim to estimate the depth of subsurface cavities from gravity data by a new method of 2D inversion through a Multi Layer Perceptron(MLP) neural network.Infact, this method is an intelligent way to interpret gravity data and gain an estimation of depth and shape. The MLP neural network was trained for two main models of cavities: sphere and cylinder in a domain of radius and depth. We tested different MLP’s with different number of neurons in the hidden layer and obtained the optimum value for number of neurons in the hidden layer. Then it was tested in present of 10% noise(S/N=.1), and also tested for real data. It presented good results for depth estimation of subsurface cavities. Keywords: gravity, 2D inversion, subsurface cavities, artificial neural networks, Multi Layer Perceptron

This paper describes four neural networks multilayer perceptron (MLP) network, Elman network, NARXSP network and radial basis function (RBF) network. Neural networks are applied for identification and control of DC servo motor and benchmark nonlinear system. Number of epochs required and time taken to train the controller are shown in the form of bar plots for four neural networks. Levenberg-Marquardt algorithm is used for training the controller using neural network toolbox in MATLAB. Each neural network controller is run ten times. Their performances are compared for each run in terms of number of epochs required and time taken to train each controller for tracking a reference trajectory.

The present work aims to study the development and application of Radial Basis Function (RBF) networks for predicting auger energy consumption based on input energy. The study utilized RBF networks and explored the input energy with treatments 2 (Soil moisture content), 2 (Rotary speeds), 2 (Hole depths) and 4 (Replication) based on field operations. As indicated by the results, energy input differed between the treatments but was not significant. The highest input value in transaction soil moisture content was 14.75 %, rotary speeds of 235 rpm, and hole depths of 40 cm. In comparison, the lower input energy at transaction soil moisture content was 7.9%, rotary speeds of 235 rpm, and hole depths of 20 cm. Input energy in treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9 %,235 rpm, and 20 cm) were 100.204 and 57.135 MJ. ha-1, respectively. The highest input energy shares were recorded for diesel fuel at all treatments. Furthermore, the RBF network with one hidden layer had good convergence. The output results showed 10 and five hidden neurons in a hidden layer with high accuracy for treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9%, 235 rpm, and 20 cm). In the treatment (14.75 %, 235 rpm, and 40 cm), the MSE for the training and testing sets was 0.0001 % and 0.01 % for data points with Ordinary RBF (ORBF type). The performance of the 3-10-1 architecture was better than other architectures. Finally, this research concluded that the RBF network method can forecast the input energy and energy expenditures related to the types of treatments.

A Multivariate Neural Network Approach to Tourism Demand Forecasting

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.

Air pollution is a challenging issue in some of the large cities in developing countries. In this regard, data interpretation is one of the most important parts of air quality management. Several methods exist to analyze air quality; among these, we applied the Multilayer Perceptron (MLP) and Radial Basis Function (RBF) methods to predict the hourly air concentration of benzene in 14 districts in the municipality of Tehran. Input data were hourly temperature, wind speed and relative humidity. Both methods determined reliable results. However, the RBF neural network performance was much closer to observed benzene data than the MLP neural network. The correlation determination resulted in 0.868 for MLP and 0.907 for RBF, while the Index of Agreement (IA) was 0.889 for MLP and 0.937 for RBF. The sensitivity analysis related to the MLP neural network indicated that the temperature had the greatest effect on prediction of benzene in comparison with the wind speed and humidity in the study area. The temperature was the most significant factor in benzene production because benzene is a volatile liquid.

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable