10 results on '"approximation error"'
Search Results
2. Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model
- Author
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Shaobo Lu
- Subjects
Multivariate statistics ,Models, Statistical ,Article Subject ,General Computer Science ,Artificial neural network ,Computer science ,General Mathematics ,General Neuroscience ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Univariate ,Neurosciences. Biological psychiatry. Neuropsychiatry ,General Medicine ,Residual ,Approximation error ,Econometrics ,Autoregressive–moving-average model ,Neural Networks, Computer ,Autoregressive integrated moving average ,Error detection and correction ,Algorithms ,RC321-571 ,Forecasting ,Research Article - Abstract
Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.
- Published
- 2021
3. On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models
- Author
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Mohammad Mahdi Molla Jafari, Yong Yang, Ai Zhilu, and Shuaishuai Zheng
- Subjects
Support Vector Machine ,Article Subject ,Mean squared error ,General Biochemistry, Genetics and Molecular Biology ,Fuzzy Logic ,Approximation error ,Vegetables ,Statistics ,Computer Simulation ,Sensitivity (control systems) ,Least-Squares Analysis ,Reliability (statistics) ,Mathematics ,Adaptive neuro fuzzy inference system ,General Immunology and Microbiology ,General Medicine ,Function (mathematics) ,Refuse Disposal ,Support vector machine ,Food ,Biofuels ,Fruit ,Outlier ,Linear Models ,Medicine ,Algorithms ,Research Article - Abstract
This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.
- Published
- 2021
4. Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes
- Author
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S.M. Alizadeh, Marischa Elveny, Tzu-Chia Chen, Meysam Hosseini, and Adedoyin Isola Lawal
- Subjects
Article Subject ,Field (physics) ,Entropy ,020209 energy ,Value (computer science) ,02 engineering and technology ,General Biochemistry, Genetics and Molecular Biology ,020401 chemical engineering ,Approximation error ,0202 electrical engineering, electronic engineering, information engineering ,Production (economics) ,Applied mathematics ,Sensitivity (control systems) ,0204 chemical engineering ,Extreme learning machine ,Mathematics ,Biodiesel ,General Immunology and Microbiology ,Process (computing) ,General Medicine ,Models, Theoretical ,Biofuels ,Medicine ,Algorithms ,Research Article - Abstract
Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to the experimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a new Machine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real database containing 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison show that this ML method, with a mean relative error of 0.19 and R 2 values of 1, has a great performance in calculations related to the biodiesel field. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal melting point to determine isentropic compressibility.
- Published
- 2021
5. Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography
- Author
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Liya Gu, Qiusheng Shen, Yanzhe Wang, Xiaogang Ren, Xuefeng Ji, and Wenjuan Cai
- Subjects
Medicine (General) ,Article Subject ,Computed Tomography Angiography ,Biomedical Engineering ,Health Informatics ,Coronary Angiography ,computer.software_genre ,R5-920 ,Sørensen–Dice coefficient ,Approximation error ,Voxel ,Medical technology ,Humans ,Segmentation ,Sensitivity (control systems) ,R855-855.5 ,Mathematics ,business.industry ,Pattern recognition ,Coronary Vessels ,Test set ,Line (geometry) ,Surgery ,Artificial intelligence ,business ,Tomography, Spiral Computed ,Distance transform ,computer ,Algorithms ,Research Article ,Biotechnology - Abstract
This paper presents an in-depth study and analysis of the 3D arterial centerline in spiral CT coronary angiography, and constructs its detection and extraction technique. The first time, the distance transform is used to complete the boundary search of the original figure; the second time, the distance transform is used to calculate the value of the distance transform of all voxels, and according to the value of the distance transform, unnecessary voxels are deleted, to complete the initial contraction of the vascular region and reduce the computational consumption in the next process; then, the nonwitnessed voxels are used to construct the maximum inner joint sphere model and find the skeletal voxels that can reflect the shape of the original figure. Finally, the skeletal lines were optimized on these initially extracted skeletal voxels using a dichotomous-like principle to obtain the final coronary artery centerline. Through the evaluation of the experimental results, the algorithm can extract the coronary centerline more accurately. In this paper, the segmentation method is evaluated on the test set data by two kinds of indexes: one is the index of segmentation result evaluation, including dice coefficient, accuracy, specificity, and sensitivity; the other is the index of clinical diagnosis result evaluation, which is to refine the segmentation result for vessel diameter detection. The results obtained in this paper were compared with the physicians’ labeling results. In terms of network performance, the Dice coefficient obtained in this paper was 0.89, the accuracy was 98.36%, the sensitivity was 93.36%, and the specificity was 98.76%, which reflected certain advantages in comparison with the advanced methods proposed by previous authors. In terms of clinical evaluation indexes, by performing skeleton line extraction and diameter calculation on the results obtained by the segmentation method proposed in this paper, the absolute error obtained after comparing with the diameter of the labeled image was 0.382 and the relative error was 0.112, which indicates that the segmentation method in this paper can recover the vessel contour more accurately. Then, the results of coronary artery centerline extraction with and without fine branch elimination were evaluated, which proved that the coronary artery centerline has higher accuracy after fine branch elimination. The algorithm is also used to extract the centerline of the complete coronary artery tree, and the results prove that the algorithm has better results for the centerline extraction of the complete coronary vascular tree.
- Published
- 2021
6. Approximate Iteration Algorithm with Error Estimate for Fixed Point of Nonexpansive Mappings.
- Author
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Yongfu Su
- Subjects
- *
ITERATIVE methods (Mathematics) , *APPROXIMATION error , *ALGORITHMS , *FIXED point theory , *NONEXPANSIVE mappings , *STOCHASTIC convergence , *LINEAR operators - Abstract
The purpose of this article is to present a general viscosity iteration process {xn} which defined by xn + 1 = (I - anA)Txn + ßn?f(xn) + (an - ßn)xn and to study the convergence of {xn}, where T is a nonexpansive mapping and A is a strongly positive linear operator, if {an}, {ßn} satisfy appropriate conditions, then iteration sequence {xn} converges strongly to the unique solution x* ∈ f(T) of variational inequality ((A-?f)x*,x-x*) = 0,forallx ∈ f(T). Meanwhile, a approximate iteration algorithm is presented which is used to calculate the fixed point of nonexpansive mapping and solution of variational inequality, the error estimate is also given. The results presented in this paper extend, generalize, and improve the results of Xu, G. Marino and Xu and some others. [ABSTRACT FROM AUTHOR]
- Published
- 2012
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7. Methodological Framework for Estimating the Correlation Dimension in HRV Signals
- Author
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Pablo Laguna, Augusto Navarro, Raquel Bailon, Eva Rovira, Juan Bolea, and Jose Maria Remartinez
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Correlation dimension ,Article Subject ,lcsh:Computer applications to medicine. Medical informatics ,Anesthesia, Spinal ,General Biochemistry, Genetics and Molecular Biology ,Correlation ,Dimension (vector space) ,Heart Rate ,Pregnancy ,Approximation error ,Statistics ,Humans ,Point (geometry) ,Mathematics ,General Immunology and Microbiology ,Cesarean Section ,Applied Mathematics ,Reproducibility of Results ,Signal Processing, Computer-Assisted ,General Medicine ,Sigmoid function ,Lorenz system ,Exponential function ,ROC Curve ,Data Interpretation, Statistical ,Modeling and Simulation ,lcsh:R858-859.7 ,Female ,Hypotension ,Algorithms ,Software ,Research Article - Abstract
This paper presents a methodological framework for robust estimation of the correlation dimension in HRV signals. It includes (i) a fast algorithm for on-line computation of correlation sums; (ii) log-log curves fitting to a sigmoidal function for robust maximum slope estimation discarding the estimation according to fitting requirements; (iii) three different approaches for linear region slope estimation based on latter point; and (iv) exponential fitting for robust estimation of saturation level of slope series with increasing embedded dimension to finally obtain the correlation dimension estimate. Each approach for slope estimation leads to a correlation dimension estimate, calledD^2,D^2⊥, andD^2max.D^2andD^2maxestimate the theoretical value of correlation dimension for the Lorenz attractor with relative error of 4%, andD^2⊥with 1%. The three approaches are applied to HRV signals of pregnant women before spinal anesthesia for cesarean delivery in order to identify patients at risk for hypotension.D^2keeps the 81% of accuracy previously described in the literature whileD^2⊥andD^2maxapproaches reach 91% of accuracy in the same database.
- Published
- 2014
8. A Comparative Study on Improved Arrhenius-Type and Artificial Neural Network Models to Predict High-Temperature Flow Behaviors in 20MnNiMo Alloy
- Author
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Ying-ying Liu, Yu-feng Xia, Chun-tang Yu, and Guo-zheng Quan
- Subjects
Models, Molecular ,Materials science ,Compressive Strength ,Article Subject ,Correlation coefficient ,lcsh:Medicine ,Thermodynamics ,Flow stress ,lcsh:Technology ,Phase Transition ,General Biochemistry, Genetics and Molecular Biology ,Diffusion ,symbols.namesake ,Viscosity ,Nickel ,Approximation error ,Elastic Modulus ,Tensile Strength ,Alloys ,Computer Simulation ,lcsh:Science ,General Environmental Science ,Molybdenum ,Arrhenius equation ,Manganese ,lcsh:T ,business.industry ,lcsh:R ,Thermal Conductivity ,General Medicine ,Strain rate ,Atmospheric temperature range ,Kinetics ,Models, Chemical ,symbols ,lcsh:Q ,Neural Networks, Computer ,Artificial intelligence ,business ,Algorithms ,Research Article ,Test data - Abstract
The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173∼1473 K and strain rate range of 0.01∼10 s−1. Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former,Rand AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of −39.99%∼35.05% and −3.77%∼16.74%. As for the former, only 16.3% of the test data set possessesη-values within±1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.
- Published
- 2014
9. Integrating a Hive Triangle Pattern with Subpixel Analysis for Noncontact Measurement of Structural Dynamic Response by Using a Novel Image Processing Scheme
- Author
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Shih-Lin Hung, Yung Chi Lu, and Tzu Hsuan Lin
- Subjects
Digital image correlation ,Article Subject ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,lcsh:Medicine ,Image processing ,lcsh:Technology ,General Biochemistry, Genetics and Molecular Biology ,Displacement (vector) ,Approximation error ,Digital image processing ,Image Interpretation, Computer-Assisted ,Image Processing, Computer-Assisted ,Computer vision ,lcsh:Science ,General Environmental Science ,Block (data storage) ,business.industry ,lcsh:T ,Linear variable differential transformer ,lcsh:R ,General Medicine ,Subpixel rendering ,lcsh:Q ,Artificial intelligence ,business ,Algorithms ,Research Article - Abstract
This work presents a digital image processing approach with a unique hive triangle pattern by integrating subpixel analysis for noncontact measurement of structural dynamic response data. Feasibility of proposed approach is demonstrated based on numerical simulation of a photography experiment. According to those results, the measured time-history displacement of simulated image correlates well with the numerical solution. A small three-story frame is then mounted on a small shaker table, and a linear variation differential transformation (LVDT) is set on the second floor. Experimental results indicate that the relative error between data from LVDT and analyzed data from digital image correlation is below 0.007%, 0.0205 in terms of frequency and displacement, respectively. Additionally, the appropriate image block affects the estimation accuracy of the measurement system. Importantly, the proposed approach for evaluating pattern center and size is highly promising for use in assigning the adaptive block for a digital image correlation method.
- Published
- 2014
10. Improving the S-Shape Solar Radiation Estimation Method for Supporting Crop Models
- Author
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Nándor Fodor
- Subjects
Relation (database) ,Article Subject ,Climate ,lcsh:Medicine ,Radiation ,lcsh:Technology ,General Biochemistry, Genetics and Molecular Biology ,symbols.namesake ,Approximation error ,Statistics ,Solar Energy ,Calibration ,Computer Simulation ,Biomass ,lcsh:Science ,General Environmental Science ,Mathematics ,Estimation ,Models, Statistical ,Fourier Analysis ,business.industry ,lcsh:T ,lcsh:R ,Temperature ,Reproducibility of Results ,Agriculture ,General Medicine ,Models, Theoretical ,Solar energy ,United States ,Fourier analysis ,Line (geometry) ,symbols ,lcsh:Q ,business ,Algorithms ,Research Article - Abstract
In line with the critical comments formulated in relation to the S-shape global solar radiation estimation method, the original formula was improved via a 5-step procedure. The improved method was compared to four-reference methods on a large North-American database. According to the investigated error indicators, the final 7-parameter S-shape method has the same or even better estimation efficiency than the original formula. The improved formula is able to provide radiation estimates with a particularly low error pattern index ( P I d o y ) which is especially important concerning the usability of the estimated radiation values in crop models. Using site-specific calibration, the radiation estimates of the improved S-shape method caused an average of 2 . 7 2 ± 1 . 0 2 ( 𝛼 = 0 . 0 5 ) relative error in the calculated biomass. Using only readily available site specific metadata the radiation estimates caused less than 5% relative error in the crop model calculations when they were used for locations in the middle, plain territories of the USA.
- Published
- 2012
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