76 research outputs found
A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems
This paper presents a block oriented nonlinear dynamic model suitable for
online identi cation.The model has the well known Hammerstein architecture
where as a novelty the nonlinear static part is represented by a B-spline
neural network (BSNN), and the linear static one is formalized by an auto
regressive exogenous model (ARX). The model is suitable as a feed-forward
control module in combination with a classical feedback controller to regulate
velocity and position of pneumatic and hydraulic actuation systems
which present non stationary nonlinear dynamics. The adaptation of both
the linear and nonlinear parts is taking place simultaneously on a patterby-
patter basis by applying a combination of error-driven learning rules and
the recursive least squares method. This allows to decrease the amount of
computation needed to identify the model's parameters and therefore makes
the technique suitable for real time applications. The model was tested with
a silver box benchmark and results show that the parameters are converging
to a stable value after 1500 samples, equivalent to 7.5s of running time.
The comparison with a pure ARX and BSNN model indicates a substantial
improvement in terms of the RMS error, while the comparison with alternative
non linear dynamic models like the NNOE and NNARX, having the
same number of parameters but greater computational complexity, shows
comparable performances
An Overview of Electricity Demand Forecasting Techniques
Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year. Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique. Keywords: Electricity Demand, Forecasting Techniques, Soft Computing, Regression method, SVM
A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems
This paper presents a block oriented nonlinear dynamic model suitable for
online identi cation.The model has the well known Hammerstein architecture
where as a novelty the nonlinear static part is represented by a B-spline
neural network (BSNN), and the linear static one is formalized by an auto
regressive exogenous model (ARX). The model is suitable as a feed-forward
control module in combination with a classical feedback controller to regulate
velocity and position of pneumatic and hydraulic actuation systems
which present non stationary nonlinear dynamics. The adaptation of both
the linear and nonlinear parts is taking place simultaneously on a patterby-
patter basis by applying a combination of error-driven learning rules and
the recursive least squares method. This allows to decrease the amount of
computation needed to identify the model's parameters and therefore makes
the technique suitable for real time applications. The model was tested with
a silver box benchmark and results show that the parameters are converging
to a stable value after 1500 samples, equivalent to 7.5s of running time.
The comparison with a pure ARX and BSNN model indicates a substantial
improvement in terms of the RMS error, while the comparison with alternative
non linear dynamic models like the NNOE and NNARX, having the
same number of parameters but greater computational complexity, shows
comparable performances
Identification of some nonlinear systems by using least-squares support vector machines
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 112-116.The well-known Wiener and Hammerstein type nonlinear systems and their various combinations
are frequently used both in the modeling and the control of various electrical,
physical, biological, chemical, etc... systems. In this thesis we will concentrate on the
parametric identification and control of these type of systems. In literature, various identification
methods are proposed for the identification of Hammerstein and Wiener type
of systems. Recently, Least Squares-Support Vector Machines (LS-SVM) are also applied
in the identification of Hammerstein type systems. In the majority of these works, the
nonlinear part of Hammerstein system is assumed to be algebraic, i.e. memoryless. In
this thesis, by using LS-SVM we propose a method to identify Hammerstein systems
where the nonlinear part has a finite memory. For the identification of Wiener type systems,
although various methods are also available in the literature, one approach which is
proposed in some works would be to use a method for the identification of Hammerstein
type systems by changing the roles of input and output. Through some simulations it
was observed that this approach may yield poor estimation results. Instead, by using
LS-SVM we proposed a novel methodology for the identification of Wiener type systems.
We also proposed various modifications of this methodology and utilized it for
some control problems associated with Wiener type systems. We also proposed a novel
methodology for identification of NARX (Nonlinear Auto-Regressive with eXogenous inputs)
systems. We utilize LS-SVM in our methodology and we presented some results
which indicate that our methodology may yield better results as compared to the Neural
Network approximators and the usual Support Vector Regression (SVR) formulations.
We also extended our methodology to the identification of Wiener-Hammerstein type
systems. In many applications the orders of the filter, which represents the linear part of
the Wiener and Hammerstein systems, are assumed to be known. Based on LS-SVR, we
proposed a methodology to estimate true ordersYavuzer, MahmutM.S
A comparative study of surrogate musculoskeletal models using various neural network configurations
Title from PDF of title page, viewed on August 13, 2013Thesis advisor: Reza R. DerakhshaniVitaIncludes bibliographic references (pages 85-88)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The central idea in musculoskeletal modeling is to be able to predict body-level
(e.g. muscle forces) as well as tissue-level information (tissue-level stress, strain, etc.). To
develop computationally efficient techniques to analyze such models, surrogate models
have been introduced which concurrently predict both body-level and tissue-level
information using multi-body and finite-element analysis, respectively. However, this
kind of surrogate model is not an optimum solution as it involves the usage of finite
element models which are computation intensive and involve complex meshing methods
especially during real-time movement simulations. An alternative surrogate modeling
method is the use of artificial neural networks in place of finite-element models. The ultimate objective of this research is to predict tissue-level stresses
experienced by the cartilage and ligaments during movement and achieve concurrent
simulation of muscle force and tissue stress using various surrogate neural network
models, where stresses obtained from finite-element models provide the frame of
reference. Over the last decade, neural networks have been successfully implemented in
several biomechanical modeling applications. Their adaptive ability to learn from
examples, simple implementation techniques, and fast simulation times make neural networks versatile and robust when compared to other techniques. The neural network
models are trained with reaction forces from multi-body models and stresses from finite
element models obtained at the interested elements. Several configurations of static and
dynamic neural networks are modeled, and accuracies close to 93% were achieved, where
the correlation coefficient is the chosen measure of goodness. Using neural networks, the
simulation time was reduced nearly 40,000 times when compared to the finite-element
models. This study also confirms theoretical concepts that special network
configurations--including average committee, stacked generalization, and negative
correlation learning--provide considerably better results when compared to individual
networks themselves.Introduction -- Methods -- Results -- Conclusion -- Future work -- Appendix A. Various linear and non-linear modeling techniques -- Appendix B. Error analysi
Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
The realistic dynamics mathematical model of a system is very important for analyzing
a system. The mathematical system model can be derived by applying physical,
thermodynamic, and chemistry laws. But this method has some drawbacks, among
which is difficult for complex systems, sometimes is untraceable for nonlinear behavior
that almost all systems have in the real world, and requires much knowledge. Another
method is system identification which is also called experimental modeling. System
identification can be made offline, but this method has a disadvantage because the
features of a dynamic system may change over time. The parameters may vary as
environmental conditions change. It requires big data and consumes a long time. This
research introduces a developed method for online system identification based on the
Hammerstein and Wiener nonlinear block-oriented structure with the artificial neural
networks (NN) advantages and recursive weighted least squares algorithm for optimizing
neural network learning in real-time. The proposed method aimed to obtain a maximally
informative mathematical model that can describe the actual dynamic behaviors of a
system, using the DC motor as a case study. The goodness of fit validation based on
the normalized root-mean-square error (NRMSE) and normalized mean square error, and
Theil’s inequality coefficient are used to evaluate the performance of models. Based on
experimental results, for best Wiener parallel NN model and series-parallel NN model
are 93.7% and 89.48%, respectively. Best Hammerstein parallel NN polynomial based
model and series-parallel NN polynomial model are 88.75% and 93.9% respectively,
for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid
based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN
hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model
70.7% and 96.4% respectively. The best model of the developed method outperformed the
conventional NARX and NARMAX methods best model by 3.26% in terms of NRMSE
goodness of fit
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