Crankpin non-circular grinding progress error forecast and compensation based on RBF-NN

2006 ◽  
Author(s):  
Wu Ganghua ◽  
He Yongyi ◽  
Shen Nanyan ◽  
Tian Yingzhong ◽  
Li Wei
Keyword(s):  
Error Forecast

scholarly journals Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Xiaofan Wang ◽  
Lingyu Xu
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Short Term Memory ◽  
Sudden Change ◽  
Back Propagation ◽  
Red Tides ◽  
Model Errors ◽  
Chl A ◽  
Error Forecast ◽  
Concentration Prediction

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.

Short-term wind power combined forecasting based on error forecast correction

2016 ◽  
Vol 119 ◽  
pp. 215-226 ◽  
Author(s):  
Zhengtang Liang ◽  
Jun Liang ◽  
Chengfu Wang ◽  
Xiaoming Dong ◽  
Xiaofeng Miao
Keyword(s):  
Wind Power ◽  
Short Term ◽  
Combined Forecasting ◽  
Error Forecast

DEMAND FORECASTING PADA INDUSTRI JUS SIAP MINUM

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Joko Supono
Keyword(s):  
Demand Forecasting ◽  
Raw Material ◽  
Error Forecast

Industri makanan dan minuman merupakan salah satu sektor penting perekonomian Indonesia. Namun, sifatnya yang perishable membuat peramalan permintaan bagi produk ini menjadi suatu hal yang krusial. Karena skenario ini, maka perusahaan yang bergerak pada ndustri makanan dan minuman memerlukan sebuah metode peramalan permintaan yang bisa memberikan tingkat akurasi tinggi dan cepat menanggapi perubahan terhadap permintaan. Sehingga tujuan penelitian adalah menentukan metode peramalan terhadap permintaan dengan tingkat error terkecil. Dalam kasus industri minuman jus siap minum, peramalan permintaan bukan hanya sekedar pemenuhan permintaan konsumen semata, tapi lebih kearah efisiensi perencanaan produksi dan efisiensi pengaturan raw material. Dalam studi kasus yang dilakukan pada salah satu industri jus siap minum didapat dengan menggunakan metode peramalan Holt winter didapat penurunan tingkat error forecast dengan MAPE mencapai 6%.Keyword: Demand forecasting, Holt Winter, Industri jus, MAPE

scholarly journals Using a Nonlinear Forcing Singular Vector Approach to Reduce Model Error Effects in ENSO Forecasting

2019 ◽  
Vol 34 (5) ◽  
pp. 1321-1342 ◽  
Author(s):  
Tao Lingjiang ◽  
Duan Wansuo
Keyword(s):  
Lead Time ◽  
Singular Vector ◽  
Forecast Model ◽  
Complex Model ◽  
Model Errors ◽  
Initial Errors ◽  
Spring Predictability Barrier ◽  
Negative Effect ◽  
Error Forecast ◽  
Vector Approach

Abstract Nonlinear forcing singular vector (NFSV)-based assimilation is adopted to determine the model tendency errors that represent the combined effect of different kinds of model errors; then, an NFSV-tendency error forecast model is formulated. This error forecast model is coupled with an intermediate complex model (ICM) and makes the ICM output closer to the observations; finally, an NFSV-ICM forecast model for ENSO is constructed. The competing aspect of the NFSV-ICM is to consider not only model errors but also the interaction between model errors and initial errors because of the mathematical nature of the NFSV-tendency errors. Based on the prediction experiments for tropical SSTAs during either the training period (1960–96; i.e., when the NFSV-ICM is formulated) or the cross-validation period (1997–2016), the NFSV-ICM is determined to have a much higher forecast skill in predicting ENSO that, specifically, extends the skillful predictions of ENSO from a lead time of 6 months in the original ICM to a lead time of 12 months. The higher skill of the NFSV-ICM is especially reflected in the predictions of SSTAs in the central and western Pacific. For the well-known spring predictability barrier (SPB) phenomenon that greatly limits ENSO forecasting skill, the NFSV-ICM also shows great abilities in suppressing its negative effect on ENSO predictions. Although the NFSV-ICM is presently only involved with the NFSV-related assimilation of SSTs, it has shown its usefulness in predicting ENSO. It is clear that the NFSV-based assimilation approach is effective in dealing with the effect of model errors on ENSO forecasts.

scholarly journals Box-Jenkins transfer function framework applied to saving-investment nexus in the South African context

2015 ◽  
Vol 4 (1) ◽  
pp. 63-77
Author(s):  
Ntebogang Dinah Moroke
Keyword(s):  
South Africa ◽  
South African ◽  
Unit Root ◽  
Capital Mobility ◽  
Unit Root Test ◽  
Whitening Process ◽  
Full Capacity ◽  
African Context ◽  
The Relationship ◽  
Error Forecast

This paper studied the relationship between investment and savings in South Africa for the period 1990 quarter 1 to 2014 quarter 3. The unit root test confirmed the non-stationarity of the series prior to first differencing. The correlation coefficient and the model assessing a full capacity mobility hypothesis were significant and passed all the diagnostic examinations. The estimated parameter provided evidence of imperfect capital mobility. ARIMAX (5, 1, 0) out-performed all the five models and was used for pre-whitening process. This model was later used to produce a two year forecasts of investment. The error forecast measure provided enough evidence to conclude that ARIMAX (5, 1, 0) provided valid forecasts. These results are recommended when embarking on future saving-investment plans in South Africa.

Adaptive model for a software test to calculate a residual error forecast

Computing ◽  
1989 ◽  
Vol 42 (2-3) ◽  
pp. 141-158
Author(s):  
P. Wildenauer ◽  
L. Eisner
Keyword(s):  
Residual Error ◽  
Software Test ◽  
Adaptive Model ◽  
Error Forecast

A HYBRID FILTERING AND ERROR FORECAST PROCEDURE FOR DATA ASSIMILATION IN REAL-TIME FORECASTING

2004 ◽  
pp. 1237-1244
Author(s):  
HENRIK MADSEN ◽  
JACOB TORNFELDT SØRENSEN ◽  
CLAUS SKOTNER
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Hybrid Filtering ◽  
Error Forecast
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