scholarly journals Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Support Vector Machine ◽  
Soft Computing ◽  
Prediction Models ◽  
Base Isolation ◽  
Least Square ◽  
Support Vector ◽  
Isolation System ◽  
Inference System ◽  
Comparison Results ◽  
Soft Computing Techniques

Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB) isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.

scholarly journals Comparison of six different soft computing methods in modeling evaporation in different climates

2016 ◽  
Author(s):  
Lunche Wang ◽  
Ozgur Kisi ◽  
Mohammad Zounemat-Kermani ◽  
Yiqun Gan
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Least Square ◽  
Support Vector ◽  
Climatic Data ◽  
Regression Methods ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Fuzzy Genetic ◽  
Different Climates

Abstract. Evaporation plays important roles in regional water resources management,terrestrial ecological process and regional climate change. This study investigated the abilities of six different soft computing methods, Multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at eight stations in different climates, air temperature (Ta), solar radiation (Rg), sunshine hours (Hs), relative humidity (RH) and wind speed (Ws) during 1961–2000 are used for model development and validation. The first part of applications focused on testing and comparing the model accuracies using different local input combinations. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations, while GRNN model performed better in Tibetan Plateau. The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. Generalized models were also developed and tested with data of eight stations. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ, CQ and HK stations.

scholarly journals Prediction of Ultimate Bearing Capacity of Cohesionless Soils Using Soft Computing Techniques

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
S. Adarsh ◽  
R. Dhanya ◽  
G. Krishna ◽  
R. Merlin ◽  
J. Tina
Keyword(s):  
Bearing Capacity ◽  
Soft Computing ◽  
Ultimate Bearing Capacity ◽  
Support Vector ◽  
Width Ratio ◽  
Cohesionless Soils ◽  
Inference System ◽  
Theoretical Approaches ◽  
Soft Computing Techniques ◽  
Gp Model

This study examines the potential of two soft computing techniques, namely, support vector machines (SVMs) and genetic programming (GP), to predict ultimate bearing capacity of cohesionless soils beneath shallow foundations. The width of footing (), depth of footing (), the length-to-width ratio () of footings, density of soil ( or ), angle of internal friction (), and so forth were used as model input parameters to predict ultimate bearing capacity (). The results of present models were compared with those obtained by three theoretical approaches, artificial neural networks (ANNs), and fuzzy inference system (FIS) reported in the literature. The statistical evaluation of results shows that the presently applied paradigms are better than the theoretical approaches and are competing well with the other soft computing techniques. The performance evaluation of GP model results based on multiple error criteria confirms that GP is very efficient in accurate prediction of ultimate bearing capacity cohesionless soils when compared with other models considered in this study.

scholarly journals A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer

Foods ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1078
Author(s):  
Shagor Sarkar ◽  
Jayanta Kumar Basak ◽  
Byeong Eun Moon ◽  
Hyeon Tae Kim
Keyword(s):  
Support Vector Machine ◽  
Correlation Coefficient ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Soluble Solids ◽  
Support Vector ◽  
Soluble Solids Content ◽  
Support Vector Machine Regression ◽  
Solids Content

Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

scholarly journals NEURO-FUZZY APPROACH TO PREDICTIVE MODELING OF EMISSIONS FROM BIODIESEL POWERED TRANSIT BUSES

Transport ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 334-352 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan ◽  
Abhisek Mudgal ◽  
Shauna Hallmark
Keyword(s):  
Soft Computing ◽  
Real World ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Measuring Device ◽  
Transit Buses ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The rise in freight passenger transportation is responsible for air pollution, green house gas emissions (especially CO2) and high fuel demand. New engine technology and fuels are discovered and tested throughout the world. Biodiesel, an alternative for diesel, has been seen as a solution. However, the amount of emissions generated by a biodiesel fueled vehicle has not been understood well since most research studies of this kind reported in the literature were conducted in the laboratory. In the present study, emissions (NOx, HC, CO, CO2 and PM) were measured from biodiesel fueled transit buses using an on-road emissions measuring device known as the Portable Emissions Measurement System (PEMS). On-road study is important in terms of understanding the amount of emissions generated under the real traffic and environmental conditions. Emissions were measured on buses fueled with regular diesel (B0), B10 blend (10% biodiesel + 90% diesel) and B20 blend (20% biodiesel + 80% diesel). This paper demonstrates the use of hybrid soft-computing techniques such as the neuro-fuzzy technique for developing emissions prediction models from real-world data. Hybrid soft-computing techniques have been shown to work well in handling data prone to noise and uncertainty, which is characteristic of real-world scenario. Two neuro-fuzzy methodologies were considered in this study: the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS). A brief review of model development, recommended parametric settings, and statistical evaluation of prediction performance of both techniques are discussed. In general, the ANFIS showed better prediction accuracy for the individual emissions compared to DENFIS although the prediction accuracies are comparable.

scholarly journals Prediction of BP Reactivity to Talking Using Hybrid Soft Computing Approaches

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Gurmanik Kaur ◽  
Ajat Shatru Arora ◽  
Vijender Kumar Jain
Keyword(s):  
Prediction Models ◽  
Statistical Tests ◽  
Least Square ◽  
Predictor Variables ◽  
Percentage Error ◽  
Support Vector ◽  
Inference System ◽  
Biological Variables ◽  
Increased Risk ◽  
Svm Model

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Estimation of discharge correction factor of modified Parshall flume using ANFIS and ANN

2020 ◽  
Vol 1 (105) ◽  
pp. 17-30
Author(s):  
D. Saran ◽  
N.K. Tiwari
Keyword(s):  
Sensitivity Analysis ◽  
Soft Computing ◽  
Correction Factor ◽  
Prediction Models ◽  
Training Dataset ◽  
Inference System ◽  
Testing Dataset ◽  
Soft Computing Techniques ◽  
Regression Techniques ◽  
Prediction Capability

Purpose: To evaluate and compare the capability of ANFIS (Adaptive Neuro-Fuzzy-Inference System), ANN (Artificial Neural Network), and MNLR (Multiple Non-linear Regression) techniques in the estimation and formulation of Discharge Correction Factor (Cd) of modified Parshall flumes as based on linear relations and errors between input and output data. Design/methodology/approach: Acknowledging the necessity of further research in this field, experiments were conducted in the Hydraulics Laboratory of Civil Engineering Department, National Institute of Technology, Kurukshetra, India. The Parshall flume characteristics, associated longitudinal slopes and the discharge passing through the flume were varied. Consequent water depths at specific points in Parshall flumes were noted and the values of Cd were computed. In this manner, a data set of 128 observations was acquired. This was bifurcated arbitrarily into a training dataset consisting of 88 observations and a testing dataset consisting of 40 observations. Models developed using the training dataset were checked on the testing dataset for comparison of the performance of each predictive model. Further, an empirical relationship was formulated establishing Cd as a function of flume characteristics, longitudinal slope, and water depth at specific points using the MNLR technique. Moreover, Cd was estimated using soft computing tools; ANFIS and ANN. Finally, a sensitivity analysis was done to find out the flume variable having the greatest influence on the estimation of Cd. Findings: The predictive accuracy of the ANN-based model was found to be better than the model developed using ANFIS, followed by the model developed using the MNLR technique. Further, sensitivity analysis results indicated that primary depth reading (Ha) as input parameter has the greatest influence on the prediction capability of the developed model. Research limitations/implications: Since the soft computing models are data based learning, hence the prediction capability of these models may dwindle if data is selected beyond the current data range, which is based on the experiments conducted under specific conditions. Further, since the present study has faced time and facility constraints, hence there is still a huge scope of research in this field. Different lateral slopes, combined laterallongitudinal slopes, and more modified Parshall flume models of larger sizes can be added to increase the versatility of the current research. Practical implications: Cd of modified Parshall flumes can be predicted using the ANNbased prediction model more accurately as compared to other considered techniques. Originality/value: The comparative analysis of prediction models, as well as the formulation of relation, has been conducted in this study. In all the previous works, little to no soft computing techniques have been applied for the analysis of Parshall flumes. Even the regression techniques have been applied only on Parshall flumes of standard sizes. However, this paper includes not only Parshall flume of standard size but also a modified Parshall flume in its pursuit of predicting Cd with the help of ANN and ANFIS based prediction models along with MNLR technique

Enhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine

2019 ◽  
Vol 13 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Hossam Talaat Elshambaky
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Mean Square Error ◽  
Soft Computing ◽  
Support Vector ◽  
Mean Square ◽  
Least Squares Collocation ◽  
Soft Computing Techniques ◽  
Error Terms ◽  
The Mean

Abstract Least-squares collocation (LSC) is a crucial mathematical tool for solving many geodetic problems. It has the capability to adjust, filter, and predict unknown quantities that affect many geodetic applications. Hence, this study aims to enhance the predictability property of LSC through applying soft computing techniques in the stage of describing the covariance function. Soft computing techniques include the support vector machine (SVM), least-squares-support vector machine (LS-SVM), and artificial neural network (ANN). A real geodetic case study is used to predict a national geoid from the EGM2008 global geoid model in Egypt. A comparison study between parametric and soft computing techniques was performed to assess the LSC predictability accuracy. We found that the predictability accuracy increased when using soft computing techniques in the range of 10.2 %–27.7 % and 8.2 %–29.8 % based on the mean square error and the mean error terms, respectively, compared with the parametric models. The LS-SVM achieved the highest accuracy among the soft computing techniques. In addition, we found that the integration between the LS-SVM with LSC exhibits an accuracy of 20 % and 25 % higher than using LS-SVM independently as a predicting tool, based on the mean square error and mean error terms, respectively. Consequently, the LS-SVM integrated with LSC is recommended for enhanced predictability in geodetic applications.

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