The Volterra adaptive prediction method based on matrix decomposition

Images are an important part of today's digital world. However, due to the large quantity of data needed to represent modern imagery the storage of such data can be expensive. Thus, work on efficient image storage (image compression) has the potential to reduce storage costs and enable new applications.This lossless image compression has uses in medical, scientific and professional video processing applications.Compression is a process, in which given size of data is compressed to a smaller size. Storing and sending images to its original form can present a problem in terms of storage space and transmission speed.Compression is efficient for storing and transmission purpose.In this paper we described a new lossless adaptive prediction based algorithm for continuous tone images. In continuous tone images spatial redundancy exists.Our approach is to develop a new backward adaptive prediction techniques to reduce spatial redundancy in a image.The new prediction technique known as Modifed Gradient Adjusted Predictor (MGAP) is developed. MGAP is based on the prediction method used in Context based Adaptive Lossless Image Coding (CALIC). An adaptive selection method which selects the predictor in a slope bin in terms of minimum entropy improves the compression performance.

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An adaptive prediction method for signal fusion

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2017.8037432 ◽

2017 ◽

Author(s):

Alexander Holland ◽

Shadnaz Asgari

Keyword(s):

Prediction Method ◽

Signal Fusion ◽

Adaptive Prediction

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A novel multi-step adaptive prediction method for chaotic time series

Acta Physica Sinica ◽

10.7498/aps.55.1666 ◽

2006 ◽

Vol 55 (4) ◽

pp. 1666

Author(s):

Meng Qing-Fang ◽

Zhang Qiang ◽

Mu Wen-Ying

Keyword(s):

Time Series ◽

Prediction Method ◽

Chaotic Time Series ◽

Adaptive Prediction

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PERFORMANCE EVALUATION AND COMPARATIVE ANALYSIS OF WATERMARKING ALGORITHM BASED ON ADAPTIVE PREDICTION METHOD

ICTACT Journal on Image and Video Processing ◽

10.21917/ijivp.2018.0266 ◽

2018 ◽

Vol 9 (2) ◽

pp. 1876-1881

Author(s):

Chetna Sharma ◽

Neeraj Jain

Keyword(s):

Performance Evaluation ◽

Comparative Analysis ◽

Prediction Method ◽

Adaptive Prediction

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Chaotic Time Series Adaptive Prediction Based on Volterra Series

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.2495 ◽

2014 ◽

Vol 945-949 ◽

pp. 2495-2498 ◽

Cited By ~ 1

Author(s):

Fang Dai ◽

Gao Hua Liao

Keyword(s):

Time Series ◽

Real Time ◽

Volterra Series ◽

Prediction Method ◽

Fault Prediction ◽

Statistical Prediction ◽

Neighborhood Search ◽

Model Parameters ◽

Current Time ◽

Adaptive Prediction

At present, the mine has only realized the real-time monitoring of gas, but not the prediction of gas.There were some limitation of the traditional prediction method, such as modeling subjectivism and statistical prediction. Because it can dynamically adjust the parameters of the model, adaptive prediction method can get the current time according to the prediction error of data and the current time, real-time fault prediction model parameters, this is a very consistent with the prediction method for practical use.This paper presents the gas emission chaos time series method by using volterra series prediction, and on the basis to establish time-series prediction models. The results show that the method not only avoids the phase space reconstruction, but also avoid the points in the neighborhood search, in real-time, with very high efficiency.

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Drought prediction based on an improved VMD-OS-QR-ELM model

PLoS ONE ◽

10.1371/journal.pone.0262329 ◽

2022 ◽

Vol 17 (1) ◽

pp. e0262329

Author(s):

Yang Liu ◽

Li Hu Wang ◽

Li Bo Yang ◽

Xue Mei Liu

Keyword(s):

Prediction Models ◽

Prediction Method ◽

Triangular Matrix ◽

Matrix Decomposition ◽

Support Vector ◽

Support Vector Regression Model ◽

Drought Prediction ◽

Mode Decomposition ◽

Learning Machine ◽

Hidden Layer

To overcome the low accuracy, poor reliability, and delay in the current drought prediction models, we propose a new extreme learning machine (ELM) based on an improved variational mode decomposition (VMD). The model first redefines the output of the hidden layer of the ELM model with orthogonal triangular matrix decomposition (QR) to construct an orthogonal triangular ELM (QR-ELM), and then introduces an online sequence learning mechanism (OS) into the QR-ELM to construct an online sequence OR-ELM (OS-QR-ELM), which effectively improves the efficiency of the ELM model. The mutual information extension method was then used to extend both ends of the original signal to improve the VMD end effect. Finally, VMD and OS-QR-ELM were combined to construct a drought prediction method based on the VMD-OS-QR-ELM. The reliability and accuracy of the VMD-OS-QR-ELM model were improved by 86.19% and 93.20%, respectively, compared with those of the support vector regression model combined with empirical mode decomposition. Furthermore, the calculation efficiency of the OS-QR-ELM model was increased by 88.65% and 85.32% compared with that of the ELM and QR-ELM models, respectively.

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