ANN Architecture Specifications for Modelling of Open-Cell Aluminum under Compression

The knowledge on strength properties of porous metals in compression is essential in tailored application design, as well as in elaboration of general material models. In this article, the authors propose specification details of the ANN architecture for adequate modelling of the phenomenon of compressive behaviour of open-cell aluminum. In the presented research, an algorithm was used to build different structures of artificial neural networks (ANNs), which approximated stress-strain relations of an aluminum sponge subjected to compression. Next, the quality of the built approximations was appraised. The mean absolute relative error (MARE), coefficient of determination between outputs and targets R2, root mean square error (RMSE), and mean square error (MSE) were assumed as criterial measures for the assessment of the fitting quality. The studied neural networks (NNs) were two-layer feedforward networks with different numbers of neurons in the hidden layer. A set of experimental stress-strain data from quasistatic uniaxial compression tests of open-cell aluminum of various apparent densities was used as data for training of neural networks. Analysis was performed in two modes: in the first one, all samples were taken for training, and in the second case, one sample was left out during training in order to play the role of external data for testing the trained network later. The taken out samples were maximum and minimum density samples (for extrapolation) and one random from within the density interval. The results showed that good approximation on the engineering level MARE<5% was reached for teaching networks with ≥7 neurons in the hidden layer for the first studied case and with ≥8 neurons for the second. Calculations on external data proved that 8 neurons are enough to actually obtain MARE<10%. Moreover, it was shown that the quality of approximation can be significantly improved to MARE≈7% (tested on external data) if the initial region of the stress-strain relation is modelled by an additional network.

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Prediction of hydrological time-series using extreme learning machine

Journal of Hydroinformatics ◽

10.2166/hydro.2015.020 ◽

2015 ◽

Vol 18 (2) ◽

pp. 345-353 ◽

Cited By ~ 14

Author(s):

Md Atiquzzaman ◽

Jaya Kandasamy

Keyword(s):

Neural Networks ◽

Time Series ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Extreme Learning Machine ◽

Mean Square ◽

Feed Forward Neural Networks ◽

Learning Machine ◽

Hidden Layer

Applying feed-forward neural networks has been limited due to the use of conventional gradient-based slow learning algorithms in training and iterative determination of network parameters. This paper demonstrates a method that partly overcomes these problems by using an extreme learning machine (ELM) which predicts the hydrological time-series very quickly. ELMs, also called single-hidden layer feed-forward neural networks (SLFNs), are able to well generalize the performance for extremely complex problems. ELM randomly chooses a single hidden layer and analytically determines the weights to predict the output. The ELM method was applied to predict hydrological flow series for the Tryggevælde Catchment, Denmark and for the Mississippi River at Vicksburg, USA. The results confirmed that ELM's performance was similar or better in terms of root mean square error (RMSE) and normalized root mean square error (NRMSE) compared to ANN and other previously published techniques, namely evolutionary computation based support vector machine (EC-SVM), standard chaotic approach and inverse approach.

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Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210066 ◽

2021 ◽

Vol 25 (2) ◽

pp. 227-234

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Water Table ◽

Environmental Sustainability ◽

Model Development ◽

Water Table Depth ◽

Water Levels ◽

Coefficient Of Determination ◽

Mean Square ◽

Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽

10.3390/s21051631 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1631

Author(s):

Bruno Guilherme Martini ◽

Gilson Augusto Helfer ◽

Jorge Luis Victória Barbosa ◽

Regina Célia Espinosa Modolo ◽

Marcio Rosa da Silva ◽

...

Keyword(s):

Technology Acceptance ◽

Mean Square Error ◽

Ease Of Use ◽

Coefficient Of Determination ◽

Square Root ◽

Mean Square ◽

Use Context ◽

The Mean ◽

Acceptance Model ◽

The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

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Quality of wood in the stands of poplar clones

Journal of Forest Science ◽

10.17221/791-jfs ◽

2008 ◽

Vol 54 (No. 1) ◽

pp. 9-16

Author(s):

R. Petráš ◽

J. Mecko ◽

V. Nociar

Keyword(s):

Root Mean Square Error ◽

Systematic Error ◽

Mean Square Error ◽

Statistical Tests ◽

Mean Square ◽

Poplar Clones ◽

Mean Diameter ◽

Empirical Material ◽

Relative Root

The results obtained in research on the quality of raw timber by means of the structure of assortments for the stands of poplar clones Robusta and I-214 are presented in the paper. Models for an estimation of the structure of basic assortments of poplar stands were constructed separately for each clone in dependence on mean diameter, quality of stems, and damage to stems in the stand. The clone Robusta has higher proportions of higher-quality assortments than the clone I-214. The accuracy of models was determined on empirical material. It was confirmed by statistical tests that the models did not have a systematic error. The relative root mean-square error for main assortments of the clone I-214 is 15–27% and Robusta 13–24%.

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Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽

10.3390/pr9071166 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1166

Author(s):

Bashir Musa ◽

Nasser Yimen ◽

Sani Isah Abba ◽

Humphrey Hugh Adun ◽

Mustafa Dagbasi

Keyword(s):

Support Vector Regression ◽

Mean Square Error ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Load Forecasting ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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Comparison of Thresholding and Edge Detection Segmentation Techniques

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.2783 ◽

2013 ◽

Vol 860-863 ◽

pp. 2783-2786

Author(s):

Yu Bing Dong ◽

Hai Yan Wang ◽

Ming Jing Li

Keyword(s):

Image Segmentation ◽

Root Mean Square Error ◽

Edge Detection ◽

Mean Square Error ◽

Root Mean Square ◽

Experimental Results ◽

Mean Square ◽

Segmentation Method ◽

Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

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Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction

Intelligent Decision Technologies ◽

10.3233/idt-200093 ◽

2021 ◽

pp. 1-9

Author(s):

Rajashree Dash ◽

Anuradha Routray ◽

Rasmita Dash ◽

Rasmita Rautray

Keyword(s):

Mean Square Error ◽

Optimization Technique ◽

Absolute Error ◽

Time Frame ◽

Gold Price ◽

Future Price ◽

Mean Square ◽

Learning Speed ◽

Predictor Model ◽

Hidden Layer

Predicting future price of Gold has always been an intriguing field of investigation for researchers as well as investors who desire to invest in present and gain profit in the future. Since ancient time, Gold is being arbitrated as a leading asset in monetary business. As the worth of gold changes within confined boundaries, reducing the effect of inflation, so it is a beneficial property favoured by many stakeholders. Hence, there is always an urge of a more authenticate model for forecasting the gold price based upon the changes in it in a previous time frame. This study focuses on designing an efficient predictor model using a Pi-Sigma Neural Network (PSNN) for forecasting future gold. The underlying motivation of using PSNN is its quick learning and easy implementation compared to other neural networks. The fixed unit weights used in between hidden and output layer of PSNN helps it in achieving faster learning speed compared to other similar types of networks. But estimating the unknown weights used in between the input and hidden layer is still a major challenge in its design phase. As final outcome of the network is highly influenced by its weight, so a novel Crow Search based nature inspired optimization algorithm (CSA) is proposed to estimate these adjustable weights of the network. The proposed model is also compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of PSNN. The model is validated over two historical datasets such as Gold/INR and Gold/AED by considering three statistical errors such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Empirical observations clearly show that, the developed CSA-PSNN predictor model is providing better prediction results compared to PSO-PSNN and DE-PSNN model.

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On-the-Go Grapevine Yield Estimation Using Image Analysis and Boolean Model

Journal of Sensors ◽

10.1155/2018/9634752 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 6

Author(s):

Borja Millan ◽

Santiago Velasco-Forero ◽

Arturo Aquino ◽

Javier Tardaguila

Keyword(s):

Image Analysis ◽

Boolean Model ◽

Coefficient Of Determination ◽

Mean Square ◽

Yield Estimation ◽

Model Image ◽

Mean Error ◽

Cluster Area ◽

The Relationship

This paper describes a new methodology for noninvasive, objective, and automated assessment of yield in vineyards using image analysis and Boolean model. Image analysis, as an inexpensive and noninvasive procedure, has been studied for this purpose, but the effect of occlusions from the cluster or other organs of the vine has an impact that diminishes the quality of the results. To reduce the influence of the occlusions in the estimation, the number of berries was assessed using the Boolean model. To evaluate the methodology, three different datasets were studied: cluster images, manually acquired vine images, and vine images captured on-the-go using a quad. The proposed algorithm estimated the number of berries in cluster images with a root mean square error (RMSE) of 20 and a coefficient of determination (R2) of 0.80. Vine images manually taken were evaluated, providing 310 grams of mean error and R2=0.81. Finally, images captured using a quad equipped with artificial light and automatic camera triggering were also analysed. The estimation obtained applying the Boolean model had 610 grams of mean error per segment (three vines) and R2=0.78. The reliability against occlusions and segmentation errors of the Boolean model makes it ideal for vineyard yield estimation. Its application greatly improved the results when compared to a simpler estimator based on the relationship between cluster area and weight.

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Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/jtde.v9n4.370 ◽

2021 ◽

Vol 9 (4) ◽

pp. 110-126

Author(s):

Wafa Benaatou ◽

Adnane Latif ◽

Vicent Pla

Keyword(s):

Power Consumption ◽

Mean Square Error ◽

Fuzzy Inference ◽

Hybrid Approach ◽

Mobile Terminal ◽

Base Station ◽

Coefficient Of Determination ◽

Mean Square ◽

Statistical Parameters ◽

Inference System

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.

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Financial Time Series Image Algorithm Based on Wavelet Analysis and Data Fusion

Journal of Sensors ◽

10.1155/2021/5577852 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Wuwei Liu ◽

Jingdong Yan

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Mean Square Error ◽

Financial Time Series ◽

Prediction Method ◽

Mean Square ◽

Rbf Network ◽

Network Prediction ◽

The Mean ◽

Hidden Layer

In recent years, people are more and more interested in time series modeling and its application in prediction. This paper mainly discusses a financial time series image algorithm based on wavelet analysis and data fusion. In this research, we conducted an in-depth study on the scale decomposition sequence and wavelet transform sequence in different scale domains of wavelet transform according to the scale change rule based on wavelet transform. We use wavelet neural network with different input neurons and hidden neurons to predict, respectively. Finally, the prediction results are integrated into the final prediction results based on the original time series by using wavelet reconstruction technology. Using RBF algorithm in neural network and SPSS Clementine, the wavelet transform sequences on five scales are modeled. Each network model has three layers: one input layer, one hidden layer, and one output layer, and each output layer has only one output element. In order to compare the prediction effect of the model proposed in this study, the ordinary RBF network is used to model and predict the log yield itself. When the input sample is 5, the minimum mean square error is obtained when the hidden layer is 6, and the mean square error is 1.6349. The mean square error of the training phase is 0.0209, and the validation error is 1.6141. The results show that the prediction results of the wavelet prediction method combined with the RBF network prediction method are better than those of wavelet prediction or RBF network prediction.

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