scholarly journals Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1143 ◽  
Author(s):  
Xijun Ye ◽  
Yingfeng Wu ◽  
Liwen Zhang ◽  
Liu Mei ◽  
Yunlai Zhou
Keyword(s):  
Principal Components ◽  
Goodness Of Fit ◽  
Hypothesis Test ◽  
Principal Component ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Goodness Of Fit Test ◽  
Fruit Fly Optimization ◽  
New Tv

The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.

scholarly journals Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2226 ◽  
Author(s):  
Ming-Wei Li ◽  
Jing Geng ◽  
Wei-Chiang Hong ◽  
Yang Zhang
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Strong Nonlinearity ◽  
Electric Load ◽  
Large Power ◽  
Fruit Fly Optimization

Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong nonlinearity, multiple factors, and large fluctuation, which lead to it being difficult to receive more accurate forecasting performances. To solve the abovementioned characteristics of a MEL time series, the least squares support vector machine (LS-SVR) hybridizing with meta-heuristic algorithms is applied to simulate the nonlinear system of a MEL time series. As it is known that the fruit fly optimization algorithm (FOA) has several embedded drawbacks that lead to problems, this paper applies a quantum computing mechanism (QCM) to empower each fruit fly to possess quantum behavior during the searching processes, i.e., a QFOA algorithm. Eventually, the cat chaotic mapping function is introduced into the QFOA algorithm, namely CQFOA, to implement the chaotic global perturbation strategy to help fruit flies to escape from the local optima while the population’s diversity is poor. Finally, a new MEL forecasting method, namely the LS-SVR-CQFOA model, is established by hybridizing the LS-SVR model with CQFOA. The experimental results illustrate that, in three datasets, the proposed LS-SVR-CQFOA model is superior to other alternative models, including BPNN (back-propagation neural networks), LS-SVR-CQPSO (LS-SVR with chaotic quantum particle swarm optimization algorithm), LS-SVR-CQTS (LS-SVR with chaotic quantum tabu search algorithm), LS-SVR-CQGA (LS-SVR with chaotic quantum genetic algorithm), LS-SVR-CQBA (LS-SVR with chaotic quantum bat algorithm), LS-SVR-FOA, and LS-SVR-QFOA models, in terms of forecasting accuracy indexes. In addition, it passes the significance test at a 97.5% confidence level.

scholarly journals Support Vecztor Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

2019 ◽  
Author(s):  
Saeed Samadianfard ◽  
Salar Jarhan ◽  
Ely Salwana ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
River Flow ◽  
Mean Squared Error ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Lake Urmia ◽  
River Flows ◽  
Fruit Fly Optimization ◽  
River Flow Forecasting

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

Prediction Research on the Failure of Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Regression

2012 ◽  
Vol 614-615 ◽  
pp. 409-413 ◽  
Author(s):  
Zhi Biao Shi ◽  
Ying Miao
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Global Optimal Solution ◽  
Fruit Fly Optimization ◽  
Global Optimal

In order to solve the blindness of the parameter selection in the Support Vector Regression (SVR) algorithm, we use the Fruit Fly Optimization Algorithm (FOA) to optimize the parameters in SVR, and then propose the optimization algorithm on the parameters in SVR based on FOA to fitting and simulate the experimental data of the turbine’s failures. This algorithm could optimize the parameters in SVR automatically, and achieve ideal global optimal solution. By comparing with the commonly used methods such as Support Vector Regression and Radial Basis Function neural network, it can be shown that the forecast results of FOA_SVR more accurate and the forecast speed is the fastest.

Research on Parameter Optimization Method of v-SVRM Forecasting Model for Crane Load Spectrum

2014 ◽  
Vol 1078 ◽  
pp. 191-196
Author(s):  
Feng Yi Lu ◽  
Shuang Wang ◽  
Ge Ning Xu ◽  
Qi Song Qi
Keyword(s):  
Computational Complexity ◽  
Optimal Parameter ◽  
Forecast Model ◽  
Fruit Fly ◽  
Life Assessment ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Load Spectrum ◽  
Training Time ◽  
Fruit Fly Optimization

Precise load spectrum of crane is essential to its fatigue analysis and life assessment. The v-SVRM (v-support vector regression machine) correctly established is key to an undistorted load spectrum. Due to computational complexity, low accuracy, poor stability of the conventional model parameter selection method with v-SVRM, a fruit fly optimization algorithm with the characteristics of easy adjustment and high precision is applied. In order to make three kind parameters synchronously optimizing search, the fruit fly algorithm is improved in consideration of parameters characteristic of the crane load spectrum v-SVRM prediction model. Then, combining the improved fruit fly algorithm with penalty function and using anti-bound thought, a secondary optimization is carried out for three kind parameters. The results of examples engineering show that the optimal parameter group selected by the improved algorithm shortens the training time, reduces the computational complexity and improves the learning accuracy and generalization ability of v-SVRM model. The accuracy and stability of parameters is enhanced by secondary optimization, so as to a better robustness and versatility of v-SVRM predictive model. It also provides a new way for the establishment of efficient and convenient crane load spectrum v-SVRM forecast model.

scholarly journals Hybrid Machine Learning Model of Support Vector Machine and Fruit Fly Optimization Algorithm for Prediction of Remaining Service Life of Flexible Pavement

2019 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Adrienn Dineva ◽  
Amir Mosavi ◽  
Narjes Nabipour ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Service Life ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Flexible Pavement ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Remaining Service Life ◽  
Non Destructive

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

scholarly journals Fruitfly based Optimization for Forgery Face Detection Analysis

2020 ◽  
Vol 9 (1) ◽  
pp. 101-106
Keyword(s):  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Forgery Detection ◽  
Fruit Fly Optimization ◽  
Detection Analysis ◽  
Negative Comment ◽  
The Face ◽  
Control Devices ◽  
Minimal Effort

Computerized imaging is huge development in ongoing decades, and these pictures is being utilized in developing number of uses. These days a few virtual products are accessible that are utilized to control picture so the picture resembles the first picture. Pictures are utilized as confirmed evidence for any wrongdoing and in the event that these pictures are not veritable, at that point it will make a doubt. The accessible minimal effort equipment and programming apparatuses makes it simple to control the first pictures with no conspicuous follows. Picture falsifications are developing at a disturbing rate in different fields and has offered negative comment in tolerating the respectability and realness of the first pictures. Destroying in an advanced picture has become a difficult assignment. The reliability of the pictures has been an inquiry because of the huge development in picture control devices. The AI and enhancement calculations are utilized to get viable outcomes. In our project, forgery detection is based on Support Vector Neural Network. The pictures are gathered and the face is recognized utilizing robust skin colored based algorithm and these pictures are exposed to feature extraction, which is prepared utilizing fruit fly optimization algorithm to group the features to identify the manipulation. The metrics, accuracy, sensitivity and specificity of the image is obtained as the result.

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