scholarly journals اقتراح استعمال مبدأ اعظم دالة انتروبي POME على توزيع كاما العام في تقدير احتمالات البقاء للسكان في العراق

2016 ◽  
Vol 22 (93) ◽  
pp. 454
Author(s):  
عمر عبد المحسن علي ◽  
رغدة زياد طارق
Keyword(s):  
Maximum Likelihood ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Generalized Gamma ◽  
Economic Survey ◽  
Absolute Percent Error

المستخلص: تم في هذا البحث تقدير دالة البقاء على قيد الحياة لبيانات تعاني من اضطراب وتشويش للمسح الاجتماعي والاقتصادي للأسرة في العراق 2012 (Iraq Household Socio-Economic Survey: IHSES II 2012) لبيانات فئات خماسية العمر تتبع توزيع كاما العام (Generalized Gamma: GG). واستعملت طريقتين للأغراض التقدير والموائمة fitting وهي طريقة مبدأ اعظم دالة انتروبي Principle of Maximizing Entropy: POME  وطريقة تمهيد لامعلمية بدالة لبّية Kernel ، للتغلب على المشاكل الرياضية التي تعتري التكاملات التي يتضمنها هذا التوزيع بالذات المتمثلة بتكامل دالة كاما الناقص، هذا الى جانب استعمال الطريقة التقليدية وهي الامكان الاعظم Maximum Likelihood: ML حيث تتم المقارنة على اساس اسلوب الجهاز المركزي للإحصاء في احتساب دالة البقاء من خلال برنامج MORTPAK كقيم حقيقية. وبعد ذلك القيام بالمقارنة باستعمال معيار جذر متوسط مربعات الخطأ Root Mean Square Error: RMSE  ، ومعيار متوسط مطلق نسبة الخطأ Mean Absolute Percent Error: MAPE  . وأظهرت النتائج أفضلية طريقة الانتروبي في تقدير دالة البقاء على الطرائق الاخرى.  

DELAY PREDICTION IN IOT AND TACTILE INTERNET USING MACHINE LEARNING APPROACH

2021 ◽  
Author(s):  
А.Р. АБДЕЛЛАХ ◽  
О.А. МАХМУД ◽  
А.И. ПАРАМОНОВ ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Single Step ◽  
Mean Square ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Accuracy Measure ◽  
Delay Prediction ◽  
Absolute Percent Error

Предложены методы прогнозирования задержки в сетях интернета вещей и тактильного интернета при прогнозировании вперед на несколько шагов MSP (Multi-step ahead Prediction) и один шаг SSP (Single-step ahead Prediction). Использованы нелинейные авторегресионные рекуррентные нейронные сети с внешними входами NARX(NonlinearAutoregressive with Exogenous inputs) для временных рядов. Проведена оценка точности прогнозирования с помощью трех алгоритмов обучения нейронной сети (Trainlm, Traincgf, Trainrp) при использовании в качестве оценок точности прогнозирования среднеквадратичной ошибки RMSE(Root Mean Square Error) и средней абсолютной ошибки в процентах MAPE(Mean Absolute Percent Error). In this paper, we perform the delay prediction in IoT and tactile Internet communication networks using a multistep ahead prediction (MSP) and single-step ahead prediction (SSP) with Time Series NARX (Nonlinear AutoRegressive with eXogenous inputs) Recurrent Neural Networks. The prediction accuracy has been evaluated using three neural network training algorithms (Trainlm, Traincgf, Trainrp) using the RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percent Error) as predictive accuracy measure.

scholarly journals Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

2021 ◽  
Vol 12 (2) ◽  
pp. 1114
Author(s):  
Ahmed Samir Badawi ◽  
Siti Hajar Yusoff ◽  
Alhareth Mohammed Zyoud ◽  
Sheroz Khan ◽  
Aisha Hashim ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Numerical Methods ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Prediction Accuracy ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Mean Square ◽  
Modified Maximum Likelihood

This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘<em>k</em>’ and the Weibull scale factor ‘<em>c</em>’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X<sup>2</sup>), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.

scholarly journals Peramalan Penerbitan Ijin Mendirikan Bangunan Dengan Single Moving Average Dan Exponential Smoothing

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Yogha Pramana ◽  
Rukmi Sari Hartati ◽  
Komang Oka Saputra
Keyword(s):  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Mean Error ◽  
Absolute Percent Error

Ijin Mendirikan Bangunan adalah ijin yang diberikan oleh Kepala Daerah pada pemilik bangunan untuk mendirikan bangunan, mengubah, memperluas, mengurangi atau merawat bangunan sesuai dengan persyaratan administratif dan persyaratan teknis yang berlaku. Peramalan adalah merupakan perkiraan mengenai terjadinya suatu kejadian pada masa depan. Peramalan merupakan sebuah alat bantu yang penting dalam perencanaan yang efesien dan efektif. Prosesnya untuk mengetahui kebutuhan di masa datang antara lain kebutuhan ukuran kuantitas, kualitas, waktu dan lokasi untuk pemenuhan permintaan barang ataupun jasa. Peramalan merupakan bagian awal dari pengambilan suatu keputusan akhir. Data Ijin Mendirikan Bangunan (IMB) di hitung dengan metode Simple Moving Average dan Exponential Smoothing untuk mengetahui nilai dari Mean Error, Mean Absolute Deviation, Mean Square Error, Standar Error, Mean Absolute Percent Error.

Ill-Condition Controlled Maximum Likelihood Registration for Multiple Sensors

2014 ◽  
Vol 898 ◽  
pp. 797-801 ◽  
Author(s):  
Xu Guang Yang ◽  
Yue Wang ◽  
Xiu Ming Shan
Keyword(s):  
Maximum Likelihood ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sensor Fusion ◽  
Fusion System ◽  
Mean Square ◽  
Multiple Sensors ◽  
Sensor Registration ◽  
Ill Conditioning

Sensor registration plays an important role in multi-sensor fusion system. In practical scenarios, the performance of traditional registration algorithms degrades when the measurements are closely positioned. In this paper, we point out and analyze the ill-conditioning problem of multi-sensor maximum likelihood registration (MLR) algorithm. Then we propose an ill-condition controlled maximum likelihood registration (ICMLR) algorithm, which can solve the ill-conditioning problem by the technique of diagonal loading. Compared with MLR, the proposed algorithm demonstrates the advantages in both bias estimates and target state estimates in terms of the root mean square error (RMSE) criterion.

scholarly journals Evaluasi Prediksi Curah Hujan dengan Algoritma Backpropogation di BMKG Cilacap

2020 ◽  
Vol 2 (2) ◽  
pp. 97-109
Author(s):  
Rito Cipta ◽  
Tezhar Rayendra Trastaronny Pastika Nugraha
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Absolute Percent Error

Backpropogation atau biasa disebut dengan backprop adalah algoritma yang mempelajari tentang bagaimana cara memperkecil atau meminimkan tingkat ke-error-an dengan dimenyesuaikannya bobot berdasarkan perbedaan output dan target sesuai dengan yang diinginkan. Penelitian ini akan membahas mengenai prediksi curah hujan bulanan di BMKG Cilacap dengan algoritma Backpropagation. Memprediksi untuk masa depan kadang belum menemukan ketepatan. Oleh sebab itu, maka peramalan harus mampu mengurangi/memperkecil tingkat kesalahannya. Hasil dari penelitian tersebut menyatakan bahwa peramalan curah hujan dengan algoritma backpropagation ini akurat dengan hasil dari Mean Square Error (MSE) adalah 0,011465, Mean Absolute Percent Error (MAPE) adalah 0.3289 pada proses pelatihan jaringan. Pada penilaian MSE dan MAPE untuk proses pengujian secara keseluruhan adalah 0,011807 dan 0,050448. 

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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