Analysis of electromechanical modes using an artificial neural network

This paper aims to perform modeling of Taiwanese farm and ecotourism data using compositional models as a probabilistic approach and to compare its results with the performance of an artificial neural network approach. Authors use probabilistic compositional models together with the artificial neural network as a classifier and compare the accuracy of both approaches. The probabilistic model structure is learned using hill climbing algorithm, and the weights of multilayer feedforward artificial neural network are learned using an R implementation of H2O library for deep learning. In case of both approaches, we employ a non-exhaustive cross-validation method and compare the models. The comparison is augmented by the structure of the compositional model and basic characterization of artificial neural network. As expected, the compositional models show significant advantages in interpretability of results and (probabilistic) relations between variables, whereas the artificial neural network provides more accurate yet “black-box” model.

<span lang="EN-US">The proposed study employed a method for identifying the main contents (category/class) that a Remote Sensing Image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward Artificial Neural Network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing.</span>

This paper presents a method for incorporating a priori information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network. Known information is incorporated into the activation function of the network output layer. An algorithm is derived for backpropagating the error and updating adjustable parameters within this layer that is consistent with existing supervised learning techniques. The developed technique is applied to the identification of a dynamic system and compared with conventional feedforward artificial neural network identifier. Results exhibit an improvement in the quality of the identification model and an increase in the rate of convergence. As a practical application, a prior information is utilized for identification of switched reluctance motor characteristics on the basis of experimental measurements. The results further demonstrate that artificial neural networks employing a priori information converge faster, require fewer adjustable weights, and more accurately predict the system of interest.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Evaluation of neural network based simulation models for the prediction of site specific soil properties of the large agricultural areas will provide important technological benefits for the efficient use of land resources and Agricultural Decision Making System. In this study, an artificial intelligence model was investigated for simulation of site specific iron (Fe) and zinc (Zn) levels in the soils of apple orchard by using a feedforward multilayered Artificial Neural Network (ANN). The measurements of Fe and Zn were made for the topsoil (0-25 cm) and subsoil samples (25-50 cm) collected from forty five different coordinates within intervals of 20 x 10 m based on a grid sampling system in the east and north directions. The measured coordinate values were reflected to the input layer of the developed two input one output feedforward multilayer of the ANN model. Respectively, the measured Fe and Zn values were reflected to its output layer, and then the training stage was started. In the training, back propagation algorithms were used to get the most suitable NN structure for the prediction. As a result of varied trainings, the ANN having two hidden layers and ten neurons produced the best estimation values for site specific Fe and Zn estimations. The most suitable prediction values (R2 = 0.98 and R2 = 0.97, P<0.01) were obtained from the ANN with 10.10.1 structure for site specific Fe and Zn levels in the topsoil. When the simulated values obtained from the ANN model were compared with the measured values, it was observed that successful results were achieved based on varied training values. Hence, the ANN based model should be calibrated for varied training conditions depending on different soil conditions to get more reliable results. Key words: Articifial neural network, neural structures, iron, zinc, apple orchard.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Heat treated cast aluminum components like engine blocks and cylinder heads can develop significant amount of residual stress and distortion particularly with water quench. To incorporate the influence of residual stress and distortion in cast aluminum product design, a rapid simulation approach has been developed based on artificial neural network and component geometry characteristics. Multilayer feed-forward artificial neural network (ANN) models were trained and verified using FEA residual stress and distortion predictions together with part geometry information such as curvature, maximum dihedral angle, topologic features including node's neighbors, as well as quench parameters like quench temperature and quench media.&lt;/div&gt;&lt;/div&gt;

An inverse analysis using artificial intelligence based on the guided ultrasonic waves is proposed for effective identification of damage in thick steel beams for the purpose of structural health monitoring applications. Parameterized modeling for finite element analysis is applied to constitute the damage parameter database cost-effectively. For signal processing and feature extraction, wavelet transform is employed. A novel feature extraction technique, damage characteristic points, is applied to constitute the database for pattern recognition procedures. Using the extracted metrics, a multilayer feedforward artificial neural network under supervision of an error-backpropagation algorithm is developed and trained. The generalization performance of the artificial neural network has been examined experimentally. Results illustrate that the proposed metrics together with artificial neural network technique are powerful tools for effective identification of damage in the case of thick structures.

This paper is concerned with optical character recognition (OCR) system for Bangla conjunctive characters. A method is proposed giving emphasis on the identification of the characters using the proposed methodology. Here generalization is achieved by preprocessing the characters before presenting them to the system for classification. The pre-processing systematically functions isolating the characters from BMP images, as well as noise removal, scaling and binary image conversion. The method uses a flexible matching between sample data and training data components applying Multi-layered Feed-forward Artificial Neural Network. A number of parameters of Neural Network are estimated so that the system performance is improved in comparison with normal training. Classification of character is defined as error minimization among the possible training set. Also, a measure of the amount of distortion for this training is given. Application of Artificial Neural Network in character recognition has made the system faster with optimum performance.

This paper presents the development of several multilayer feed-forward artificial neural network (ANN) models aimed at the simulation and parametrization of semiconductor optical amplifiers (SOAs). In the direct approach, it is assumed that the SOA physical parameters are known, and the ANNs can reproduce the results of detailed SOA models up to 20 times faster. In the inverse approach, it is assumed that the SOA is a grey-box with a known model but unknown parameters, and the ANNs infer the SOA model parameters from the input-output data. These results provide efficient surrogate models to represent SOAs in future digital twins for on-line optimization of ultra-wide band transmission systems that employ the SOA as the repeater element.

Gray layer technology represents a novel method for incorporating prior information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network (ANN). A prescriptive technique is developed that sets or constrains the weights of a particular layer, designated the gray layer, according to a known or partially known function that is related in some manner to the system of interest. An algorithm is derived for backpropagating the error through the gray layer and adjusting the parameters of the gray layer that is consistent with other ANN learning techniques. Gray layer technology is applied to the identification of two different partially known dynamic systems. Results demonstrate a significant improvement in the quality of the identification model and an increase in the rate of convergence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Hypergame is a typical framework to deal with decision situations with multiple conflicting players who perceive the conflict differently and subjectively. In the face of opponent players' different outcome preferences information over the outcome space perceived by different decision makers using fuzzy linguistic values, a concept of fuzzy relational degree between different fuzzy numbers sequences is first presented in this paper, and based on fuzzy relational degree analysis, the weight of each decision maker can be evaluated and the integrated fuzzy outcome preferences can be obtained. Then, a multi-layer feedforward artificial neural network (ANN) is designed and trained to learn the criterions of comparing trapezoidal fuzzy numbers. Using the trained ANN, the integrated fuzzy outcome preferences can be ranked automatically. At last, a numerical example is provided to illustrate the proposed method.

This paper presented the approach of prediction of pressure coefficient using feed-forward multilayer artificial neural network (ANN) by the backpropagation (BP) training algorithm. The regular shape square model of 1:1:2 (length: breadth: height) and a setback model which have a 20% setback at half of the height are considered in this study. The datasets of network architecture considered by the computational fluid dynamics method (CFD). Maximum 1% error is noticed between the Cp comparison of CFD and ANNs method. The predicted mean pressure coefficient (Cp) and fluctuation wind pressure coefficient (Cp,rms) from the calculated CFD data and predicted ANN data are plotted in the contour form. The study suggested that the setback model could control the pressure on different surfaces compared to the regular shape square model. The study also decided the ANN is a powerful tool to predict the pressure coefficient for the intermediate tapping points on the building, which may reduce the time and cost of wind tunnel study.KeywordsArtificial neural network (ANN)Backpropagation (BP) neural networkComputational fluid dynamics (CFD)Pressure coefficientPressure contourSetback buildingSquare buildingTapping pointsWind load

This paper presents the optimization of one-hidden layer Artificial Neural Network (ANN) design using Evolutionary Programming (EP) for predicting the energy output of a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. In this study, the architecture and training parameters of the multi-layer feedforward back-propagation ANN model had been optimized while the prediction performance of the ANN was maximized. The proposed Evolutionary Programming-ANN (EPANN) model employs solar radiation and ambient temperature as its inputs while the sole output is the kilowatt-hour energy output produced from the grid connected PV system. The prediction performance was quantified using the average correlation coefficient and it was maximized by determining the optimum values for the number of nodes in the hidden layer, momentum rate and learning rate during an evolutionary training. Besides searching for the optimal number of nodes and optimal training parameters for each model, the highest correlation coefficient for the prediction required for the EPANN was investigated. It was found that the maximum average correlation coefficient obtained for the EPANN training is 0.9962. Besides that, the testing process produced sufficiently high correlation coefficient value of 0.9976.

Natural compounds found in mangosteen peel such as squalene and α-cubebene were proven to exhibit antimicrobial, antibacterial and anticancer activity for cancer disease treatment. Supercritical Carbon Dioxide (SC-CO2) extraction process was conducted at constant flowrate of 24 mL/min within 40 minutes and by varying temperature and pressure from 50 to 80°C and from 34.5 to 55.1 MPa, respectively. A multi-layer feedforward back-propagation Artificial Neural Network (ANN) model was developed for solubility prediction of mangosteen peel oil in SC-CO2, where input variables were temperature and pressure. An optimal ANN model consisted of one hidden layer and five neurons was obtained with minimum value of Mean Square Error (MSE) i.e. 0.011 for 48 experimental data point used. The analysis showed that the ANN prediction model have a good correlation with the experimental data in which the values of correlation coefficient (R-value) for training, validating and testing obtained are 0.933, 0.982 and 0.927 proceedings.

The purpose of this study is to develop new multilayer feed-forward artificial neural network (ANN)-based maximal oxygen uptake (VO2max) prediction models by using submaximal treadmill exercise and nonexercise data. Using 10-fold cross validation on the dataset, standard error of estimate (SEE) and multiple correlation coefficient (R) of the models are calculated. It is shown that the models including submaximal, standard nonexercise and questionnaire variables yield higher R and lower SEE than the ones including submaximal and standard nonexercise variables only. The results of ANN-based models are also compared with the ones obtained by Multiple Linear Regression (MLR) and Support Vector Machines (SVM). It is shown that ANN-based models perform better than MLR and SVM-based models for predicting VO2max.

Artificial neural network (ANN) modelling of crystallisation temperatures of nickel–phosphorus deposits