Hybrid Model of Least Squares Handover Algorithms in Wireless Networks

2007 ◽  
Author(s):  
Claudia Rinaldi ◽  
Fortunato Santucci ◽  
Carlo Fischione ◽  
Karl Henrik Johansson
Keyword(s):  
Wireless Networks ◽  
Least Squares ◽  
Hybrid Model

scholarly journals Constrained least squares detector for OFDM/SDMA-based wireless networks

2003 ◽  
Vol 2 (1) ◽  
pp. 129-140 ◽  
Author(s):  
S. Thoen ◽  
L. Deneire ◽  
L. Van der Perre ◽  
M. Engels ◽  
H. De Man
Keyword(s):  
Wireless Networks ◽  
Least Squares ◽  
Constrained Least Squares

scholarly journals River flow time series using least squares support vector machines

2011 ◽  
Vol 15 (6) ◽  
pp. 1835-1852 ◽  
Author(s):  
R. Samsudin ◽  
P. Saad ◽  
A. Shabri
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Hybrid Model ◽  
River Flow ◽  
Moving Average ◽  
Peninsular Malaysia ◽  
Support Vector ◽  
Group Method ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

scholarly journals An EMD-Based Chaotic Least Squares Support Vector Machine Hybrid Model for Annual Runoff Forecasting

Water ◽  
2017 ◽  
Vol 9 (3) ◽  
pp. 153 ◽  
Author(s):  
Xuehua Zhao ◽  
Xu Chen ◽  
Yongxin Xu ◽  
Dongjie Xi ◽  
Yongbo Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Hybrid Model ◽  
Annual Runoff ◽  
Support Vector ◽  
Runoff Forecasting

AN INTEGRATED MODEL USING WAVELET DECOMPOSITION AND LEAST SQUARES SUPPORT VECTOR MACHINES FOR MONTHLY CRUDE OIL PRICES FORECASTING

2011 ◽  
Vol 07 (02) ◽  
pp. 299-311 ◽  
Author(s):  
YEJING BAO ◽  
XUN ZHANG ◽  
LEAN YU ◽  
KIN KEUNG LAI ◽  
SHOUYANG WANG
Keyword(s):  
Crude Oil ◽  
Least Squares ◽  
Hybrid Model ◽  
Wavelet Decomposition ◽  
Integrated Model ◽  
Oil Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Combined Forecasting

In this paper, a hybrid model integrating wavelet decomposition and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, the Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. Subsequently, the final oil price forecast is obtained by reconstructing the results of the sub-series forecasts. The experimental results show that the integrated model, based on multi-scale wavelet decomposition, outperforms the traditional single-scale models. Furthermore, the proposed hybrid model is the best among all the models compared in this study. To fully integrate the advantages of several models, a combined forecasting model is presented. The study shows that the combined forecasting model is clearly better than any individual model for crude oil price forecasting.

scholarly journals Research on Adaptive Hybrid Energy Consumption Model Based on Data Driven under Variable Working Conditions

2021 ◽  
Vol 9 ◽  
Author(s):  
Yujun Su ◽  
Mingyao Zou ◽  
Cheng Jiang ◽  
Hong Qian
Keyword(s):  
Energy Consumption ◽  
Least Squares ◽  
Hybrid Model ◽  
Working Conditions ◽  
Recursive Least Squares ◽  
Compensation Model ◽  
Mechanism Model ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Least Squares Algorithm

As to the nonlinear and time-varying problems of the energy consumption model, this paper proposes an adaptive hybrid modeling method. Firstly, the recursive least squares algorithm with adaptive forgetting factor based on fuzzy algorithm and recursive least squares algorithm is used to identify the simplified mechanism energy consumption model, which solves the data saturation phenomenon and the weights of the “old and new” data during the online identification process and guarantees the adaptability of the mechanism model. Secondly, because there is a deviation between the identified model and the simplified mechanism energy consumption model, the deviation compensation model of mechanism model is established through kernel partial least squares algorithm and the model updating strategy with sliding window, which is used to update the deviation compensation model, and then the adaptive hybrid model is established by combining with the mechanism model identified online and updated deviation compensation model. Finally, the effectiveness, generalization and adaptability of the model are verified by the actual operating data of a single working condition and variable working conditions. And comparing with the mechanism model and the data model, The comparison results show that the adaptive hybrid model has higher calculation accuracy with adaptation.

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