scholarly journals Compound Grey-Logistic Model and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiao-Lan Wu ◽  
Sheng-Yuan Wang ◽  
Guo-Yin Xu
Keyword(s):  
Information Entropy ◽  
Logistic Model ◽  
Original Data ◽  
Percentage Error ◽  
Growth Data ◽  
Simulation Accuracy ◽  
Logistic Method ◽  
Share Data ◽  
Error Index ◽  
Accuracy Of Prediction

Logistic regression model is widely used in ecology and in the analysis of social economic systems, because of its good adaptability. In order to improve the measurement accuracy of logistic model, this paper proposes a new method. A compound grey-logistic model is developed to carry out the grey transformation of the original data. Practice shows that the grey transformation data has better simulation accuracy; at the same time, grey transformation can reduce the observation noise of the original data. Mean absolute percentage error index has been used to evaluate the accuracy of prediction model, and information entropy can be used to evaluate the change of information entropy of forecasting data. In this paper, three cases are used to verify the applicability of grey-logistic model. From the perspective of the type of original data, the three cases represent three different data conditions: sufficient data, insufficient data, and fragmentary data. The cases represent different related fields: market share data, economic growth data, and R&D output data. The results show that the proposed grey-logistic method can effectively carry out the population growth analysis.

scholarly journals Hybrid C&RT-Logit Models In Churn Analysis

2014 ◽  
Vol 14 (2) ◽  
pp. 37-52
Author(s):  
Mariusz Łapczyński
Keyword(s):  
Decision Trees ◽  
Logistic Model ◽  
Hybrid Approach ◽  
Logit Models ◽  
Tree Model ◽  
True Negative ◽  
Model Interpretation ◽  
True Negative Rate ◽  
Telecommunications Services ◽  
Accuracy Of Prediction

Abstract This article attempts to explain and predict the termination of relationships in telecommunications services by using the hybrid C&RT-logit model. The combination of decision trees (C&RT algorithm) with the logistic model enriches the model interpretation and sometimes improves the accuracy of prediction. Decision trees permit to detect interactions among variables and make the model resistant to outliers and to lack of data. On the other hand, the logistic model can extend the interpretation by using odds ratios. The solution delivered by the hybrid approach was compared with the decision tree model and the logistic model. Due to the difficulty in obtaining the real dataset from the Polish market, it was decided to build a model based on the data obtained from the repository http://www.dataminingconsultant.com/DMMM.htm . The models’ performance was estimated by using popular measures such as accuracy, recall, precision, true negative rate, G-mean, F measure and lift charts.

Comparison and estimation of the growth curve models of Hanwoo steer (Bos taurus coreanae)

2018 ◽  
Author(s):  
Hu-Rak Park ◽  
Seung-Hoon Eum ◽  
Seung-Hee Roh ◽  
Jakyeom Seo ◽  
Seong-Keun Cho ◽  
...  
Keyword(s):  
Body Weight ◽  
Logistic Model ◽  
Bos Taurus ◽  
Growth Models ◽  
Logistic Models ◽  
Regression Equations ◽  
Von Bertalanffy ◽  
Growth Data ◽  
Growth Ratio ◽  
Mean Square Errors

The present study was conducted to estimate and compare the three types of growth models in Hanwoo steer (Bos aurus coreanae). The Gompertz, Von Bertalanffy, and Logistic nonlinear models were used. A total of 2,239 Hanwoo steers (Bos taurus coreanae) from 6 months to 24 months old (2003 to 2014) and 8,916 growth data from the Hanwoo improvement Center were used to estimate the growth model which included three parameters. These parameters were A, mature body weight; b, growth ratio; and k, intrinsic growth rate. Regression equations using the Gompertz, Von Bertalanffy, and Logistic models were calculated as respectively. The mean square errors (MSEs) for each model were 1945.9, 1958.7, and 1935.0, respectively. The equation using the Logistic model showed the lowest value among three models. The estimated birth weights from the Gompertz, Von Bertalanffy, and Logistic models were 50.35 kg, 36.94 kg, and 74.13 kg, respectively. Furthermore, the estimated mature weights from the Gompertz, Von Bertalanffy, and Logistic models were 919.0 kg, 1043.3 kg, and 770.0 kg, respectively. In addition, the estimated age and body weight at inflection from the Gompertz, Von Bertalanffy, and Logistic models were 349.0 days and 338.1 kg, 317.9 days and 308.2 kg, and 397.8 days and 385.0 kg, respectively. Based on the results, we concluded that the regression equation using the Logistic model was the most appropriate among the growth models for measuring data. However, further studies would be needed in order to obtain more accurate parameters using a much wider period of data from birth to shipping age.

Prediction of Fatigue Crack Growth of 2024-T351 by Grey GM(1,1) Model with Rolling Check

2013 ◽  
Vol 690-693 ◽  
pp. 1779-1783 ◽  
Author(s):  
Chih Chung Ni
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Length ◽  
Crack Growth ◽  
Compact Tension ◽  
Growth Data ◽  
Alloy Plate ◽  
Loading Cycle ◽  
Crack Growth Curve ◽  
Accuracy Of Prediction

The study is focused on the investigations into applying the grey model with rolling check to the prediction of fatigue crack growth. Fatigue crack growth data of compact tension specimens made of 2024-T351 aluminum-alloy plate tested under constant-amplitude loads were carried out for verifications. The optimal values of parameter affecting the accuracy of prediction were found by variational analysis. Using four experimental crack lengths as the source series and the optimal value of parameter for modelling with rolling check, it was found almost entire fatigue crack growth curve of the specimen can be predicted accurately. Besides, the analyzed results including number of rolling check performed, loading cycle corresponding to the maximum predicted crack length, fractured cycle of specimen, cycle ratio of loading cycle and fractured cycle, and percentage of error between maximum predicted and experimental crack length for all specimens were tabulated.

scholarly journals Simulasi Sebaran Penyakit Blas Pada Tanaman Padi Menggunakan Model Spatial Multi Criteria Evaluation: Studi Kasus Kabupaten Karawang dan Purwakarta

2019 ◽  
Vol 15 (2) ◽  
pp. 59-68
Author(s):  
Busyairi Latiful Ashar ◽  
Ali Nurmansyah ◽  
Widodo Widodo
Keyword(s):  
Soil Nutrient ◽  
Blast Disease ◽  
Percentage Error ◽  
Model Case ◽  
Multi Criteria Evaluation ◽  
Cultivation Practices ◽  
History Of ◽  
Disease Dispersal ◽  
Irrigation Quality ◽  
Accuracy Of Prediction

Dispersal Simulation of Rice Blast Disease Using Spatial Multi Criteria Evalution Model: Case Study In District of Karawang and PurwakartaRice blast is caused by Pyricularia oryzae. The potential epidemic of this disease can be spatially simulated using the MCA (Multi Criteria Analysis) method based on geographical characteristics, cultivation practices, and eviromental condition. A software that can be used for MCA is SMCE (Spatial Multi Criteria Evaluation). This study was aimed to predict the spatial dispersal of blast disease using SMCE model, and identify the factors that supports the epidemic. The study was conducted in February - August 2018 in Karawang and Purwakarta District. The research methods include observing the severity of blast disease, cultivation practices and environmental conditions, and analyzing SMCE. The SMCE analysis uses rice crop maps from the Sistem Monitoring Pertanaman Padi (Simotandi), which consists of grouping factors, standardizing factors, and weighting factors. The SMCE results are a simulation map of blast disease dispersal which is then compiled with predictions of its severity. Accuracy of prediction results was evaluated by MAPE (Mean Absolute Percentage Error) based on observational data on actual disease severity. The prediction results for Karawang and Purwakarta showed means of accuration 78.16% and 73.95% respectively. In general, factors that have a strong influence on the development of blast disease include altitude, distance from source of the epidemic, history of disease in the fields, number of spores (inoculum) trapped, irrigation quality, application of herbicides, soil nutrient (N, P, K) contents and the level of soil acidity.

Modelling GM(1,1) under the Criterion of the Minimization of Mean Absolute Percentage Error

2012 ◽  
Vol 457-458 ◽  
pp. 1447-1456
Author(s):  
Hai Jun Chen ◽  
Xi Juan Lou ◽  
Jie Liu
Keyword(s):  
Optimization Problem ◽  
Initial Approximation ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Optimal Parameters ◽  
Minimax Optimization ◽  
Simulation Accuracy ◽  
Absolute Percentage Error ◽  
Library Function ◽  
Estimation Parameters

In this paper, we present two methods to estimate the parameters of the GM (1,1)model under. The criterion of the minimization of mean absolute percentage error (MAPE) (some authors called Average relative error).A linear programming method is used to optimize the whiting value of grey derivative of GM(1,1),four published articles are chosen for practical tests of this method, the results show that this method can obviously improve the simulation accuracy. Another method is that the problem of estimation parameters of GM(1,1) model is transformed into the minimax optimization problem, then use the library function fminimax in MATLAB to solve the minimax optimization problem, the same four published articles are chosen for practical tests of this modelling method, as shown in these results, this method can obtain the local optimal parameters, yield the lower MAPE than the existing method. But it is sensitive for the initial approximation and requires a good initial approximation, the results of compared with different initial approximations show that the parameters which are obtained by the former method is the better initial approximation.

scholarly journals The Price Difference and Trend Analysis of Yesso Scallop (Patinopecten yessoensis) in Changhai County, China

2021 ◽  
Vol 9 (7) ◽  
pp. 696
Author(s):  
Daomin Peng ◽  
Qian Yang ◽  
Yongtong Mu ◽  
Hongzhi Zhang
Keyword(s):  
Linear Trend ◽  
Wholesale Price ◽  
Information Criterion ◽  
Exponential Smoothing ◽  
Economic Benefits ◽  
Percentage Error ◽  
Patinopecten Yessoensis ◽  
Simulation Accuracy ◽  
Yesso Scallop ◽  
The Difference

This paper focuses on the difference in inter-group and intra-group price of Yesso scallop (Patinopecten yessoensis) and the simulation accuracy of three different exponential smoothing models in the price. Based on the farm-gate price and wholesale price data of P. yessoensis in Changhai county from January 2017 to December 2018, this study uses the Wilcoxon rank sum test to compare the inter- and intra-group price and applies simple exponential smoothing (SES), Holt’s linear trend method, and Holt-Winters’ additive method to simulate and predict the price. The results suggest that (i) to improve economic benefits, it is necessary to formulate reasonable farming area and establish low-density ecological cultivation mode; (ii) the price’s Akaike information criterion (AIC) and mean absolute percentage error (MAPE) values by the SES model are optimal, and the MAPE value is lower than 4%; and (iii) the result of SES analysis shows no obvious change from January to March 2019.

scholarly journals Modelagem otimizada de indicadores biológicos de qualidade da água

Revista DAE ◽  
2019 ◽  
Vol 221 (68) ◽  
pp. 131-141
Author(s):  
Gabriel da Costa Cantos Jerônimo ◽  
Luiz Felipe Ramos Turci ◽  
Paulo Augusto Zaitune Pamplin ◽  
Patrícia Neves Mendes
Keyword(s):  
Population Growth ◽  
Logistic Model ◽  
Growth Models ◽  
Lemna Minor ◽  
Growth Data ◽  
Model Quality ◽  
Nonlinear Growth ◽  
Statistical Indicators ◽  
Selection Of

Resumo 27/06/2018 DOI: https://doi.org/10.36659 /dae.2020.011 Turci, L. F. R Pamplin, P. A. Z https://orcid.org/0000-0001-7516-0963 https://orcid.org/0000-0001-7318-9121 O estudo de plantas aquáticas (macrófitas) é importante, uma vez que essas plantas apresentam potencial de utilização em estudos de ecotoxicologia, como bioindicadores no tratamento de águas residuárias. A mode- lagem criteriosa do crescimento dessas plantas, especificamente a Lemna minor, é útil na determinação das condições de otimização dessas aplicações; assim, deseja-se sempre obter o modelo que melhor represente a dinâmica de crescimento populacional da planta em estudo. Neste trabalho, apresenta-se uma metodologia de ajuste e seleção de modelos de crescimento não lineares com base em indicadores estatísticos que servem como avaliadores de qualidade dos modelos. Para ilustrar o uso da metodologia, foi feito o cultivo de Lemna minor em meio Steinberg e foram ajustados três modelos aos dados médios de crescimento de suas frondes, selecionando o modelo Logístico como o melhor. Palavras-chave: Modelo de crescimento populacional. Avaliadores de qualidade. Lemna minor. Abstract The study of aquatic plants (macrophytes) is important since such plants present a potential utilization in ecotoxi- cology as bioindicators, as well in wastewater treatment. The criterious growth modelling of such plants, specifically Lemna minor, is useful for the determination of the optimal conditions of mentionedin applications - so one always looks for the best model that represents the dynamic of population growth of the plant in study. This work presents a methodology of adjustment and selection of nonlinear growth models based on statistical indicators, which work as quality evaluators for the models. To illustrate this methodology, Lemna minor was grown in Steinberg environ- ment, and three models were fitted to the fronds growth data, the Logistic model was selected as the best model. Keywords: Population growth model. Quality evaluators. Lemna minor.

scholarly journals Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

2019 ◽  
Vol 9 (21) ◽  
pp. 4554 ◽  
Author(s):  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Trung Nguyen-Thoi ◽  
Prashanth Ragam ◽  
Hossein Moayedi
Keyword(s):  
State Of The Art ◽  
Promising Result ◽  
Absolute Error ◽  
Original Data ◽  
Training Data ◽  
Open Pit ◽  
Training Dataset ◽  
Percentage Error ◽  
Ann Model ◽  
Ann Models

Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

scholarly journals GWRA: grey wolf based reconstruction algorithm for compressive sensing signals

2019 ◽  
Vol 5 ◽  
pp. e217
Author(s):  
Ahmed Aziz ◽  
Karan Singh ◽  
Ahmed Elsawy ◽  
Walid Osamy ◽  
Ahmed M. Khedr
Keyword(s):  
Compressive Sensing ◽  
Mean Squared Error ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Original Data ◽  
Percentage Error ◽  
Grey Wolf Optimizer ◽  
Signal Acquisition ◽  
Grey Wolf ◽  
Reconstruction Algorithms

The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error.

scholarly journals Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1452
Author(s):  
Xuchu Jiang ◽  
Peiyao Wei ◽  
Yiwen Luo ◽  
Ying Li
Keyword(s):  
Short Term Memory ◽  
Prediction Method ◽  
Input Sequence ◽  
Absolute Error ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Air Pollutant ◽  
Percentage Error ◽  
Mode Decomposition ◽  
Concentration Prediction

The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The “CEEMDAN-FE” decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model’s performance in predicting the real-time concentrations of PM2.5.

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