Partitioning: A unifying framework for adaptive systems, I: Estimation

Lainiotis, Demetrios G.

doi:10.1109/proc.1976.10284

Cited by 310 publications

(121 citation statements)

References 53 publications

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“…In the 60s and 70s Magill [18] and Lainiotis [19] studied Kalman filter-based models to improve the accuracy of state estimation. In the context of adaptive control, switching was first proposed by Martensson [20].…”

Section: Adaptive Local Linear Modelingmentioning

confidence: 99%

Local Linear PID Controllers for Nonlinear Control

Jiang¹,

Cho²,

Erdoğmuş³

et al. 2005

Control and Intelligent Systems

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Nonlinear PID design is difficult if one approaches the problem from a global design perspective. In this paper, we propose coalescing the adaptive local linear modeling approach with traditional linear PID controller design techniques to arrive at a principled, intuitive, and simple nonlinear PID controller design methodology. The paper, in particular, discusses two local linear modeling approaches, one based on competitive linear models attached to a self-organizing map, and one based on cooperative linear models attached to the modes of a Gaussian mixture model.The system identification and closed-loop tracking performances of the proposed PID controller design methodology is demonstrated to be better than inverse and PID controllers for a global nonlinear system identifier and a global linear model respectively.

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Section: Adaptive Local Linear Modelingmentioning

confidence: 99%

Local Linear PID Controllers for Nonlinear Control

Jiang¹,

Cho²,

Erdoğmuş³

et al. 2005

Control and Intelligent Systems

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“…The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11-12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].The purpose of this study is not to introduce one more load forecasting criterion, but it focuses on applying three different methods to real electric load data and evaluating their performance.The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21].…”

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confidence: 99%

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Pappas¹

2017

IJPER

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Abstract. Electric load forecasting is a process that has to be both fast and reliable. An accurate method of load forecasting plays the most crucial role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and operational problems connected to electrical energy production and distribution. In this study real data is used and the performance of three different techniques for adaptive electric load forecasting is evaluated. The first method is a combination of the well-established multimodel partitioning filter (MMPF) implementing extended Kalman filters (EKF) with Support Vector Machines (SVM), the second is the adaptive MMPF Kalman filters (KF) model and the third one is an artificial three layer feed-forward neural network (ANN). The results indicate that all three methods are reliable, however the combination of MMPF and SVM provides a more accurate load forecasting and at the same time identifies successfully both normal periodic behavior and any unusual activity of the electric grid.Keywords: Adaptive multimodel partitioning filter (MMPF), Support vector machines (SVM), artificial neural networks (ANN), forecasting, Kalman filters (KF), electricity demand load. IntroductionElectrical energy is considered as one of the most important factors that are closely related to both economic and social development. Accurate and fast load forecasting is related to a number of operational functions, such as system safety, power system real-time control, economic planning, grid's maintenance and also fuel planning. The problem of load forecasting has been studied extensively during recent decades. Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11-12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].The purpose of this study is not to introduce one more load forecasting criterion, but it focuses on applying three different methods to real electric load data and evaluating their performance.The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21]. The data used now is not subjected to any prior -offline manipulation in order to remove weekly and annual seasonality as was done in previous cases [5]. That's why in this case the MMPF implements a bank of extended Kalman filters (EKF) with ARMA models instead of simple Kalman filters (KF) with ARMA models in order to handle data's non-linearities. MMPF with EKF combined with genetic algorithms (GA) were successfully applied in prediction of epilepsy and in the evolution of stock values using biomedical and financial data respectively [22].The second method, presented analytically in [5], impl...

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“…The major problem with the single Kalman filter is that it does not predict well when the aircraft makes an unanticipated change of flight mode such as making a maneuver, accelerating etc [17]. Many adaptive state estimation algorithms have been proposed [18], [15], [2], [28]. The Interacting Multiple Model (IMM) algorithm [3], [16] runs two or more Kalman filters that are matched to different modes of the system in parallel.…”

Section: Introductionmentioning

confidence: 99%

An efficient Associative Processor solution to an Air Traffic Control problem

Yuan

Baker

Drews

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Abstract-This paper proposes a SIMD solution to air traffic control (ATC) using an enhanced SIMD machine model called an Associative Processor (AP). This differs from previous ATC systems that are designed for MIMD computers and have a great deal of difficulty meeting the predictability requirements for ATC, which are critical for meeting the strict certification standards required for safety critical software components. The proposed SIMD solution will support accurate and meaningful predictions of worst case execution times and will guarantee all deadlines are met. Also, the software will be much simpler and smaller in size than the current corresponding ATC software. An important consequence of these features is that the V&V (Validation and Verification) process will be considerably simpler than for current ATC software. Additionally, the associative processor is enhanced SIMD hardware and is considerably cheaper and simpler than the MIMD hardware currently used to support ATC. The ClearSpeed CSX600 accelerator is used to emulate the AP model. A preliminary implementation of the proposed method has been developed and experimental results comparing MIMD and CSX600 approaches are presented. The performance of CSX600 has better scalability, efficiency, and predictability than that of MIMD.

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Partitioning: A unifying framework for adaptive systems, I: Estimation

Cited by 310 publications

References 53 publications

Local Linear PID Controllers for Nonlinear Control

Local Linear PID Controllers for Nonlinear Control

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

An efficient Associative Processor solution to an Air Traffic Control problem

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