24,533 research outputs found
A forecasting of indices and corresponding investment decision making application
Student Number : 9702018F -
MSc(Eng) Dissertation -
School of Electrical and Information Engineering -
Faculty of Engineering and the Built EnvironmentDue to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future
financial necessities. This research proposes an application, which employs computational intelligent methods that could
assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is
employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection
Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in
possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial
Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading
strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg
Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been
determined that the MLP neural network architecture is particularly suited in the prediction of closing index price
performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the
Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree
designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as
scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of
the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16
classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural
networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4
classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of
concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System
implementation of this design performed equally well
Human-in-the-Loop Model Predictive Control of an Irrigation Canal
Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].Junta de AndalucĂa P11-TEP-812
Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications
The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version
Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System
Abstract—This paper presents dynamic voltage collapse
prediction on an actual power system using support vector machines.
Dynamic voltage collapse prediction is first determined based on the
PTSI calculated from information in dynamic simulation output.
Simulations were carried out on a practical 87 bus test system by
considering load increase as the contingency. The data collected from
the time domain simulation is then used as input to the SVM in which
support vector regression is used as a predictor to determine the
dynamic voltage collapse indices of the power system. To reduce
training time and improve accuracy of the SVM, the Kernel function
type and Kernel parameter are considered. To verify the
effectiveness of the proposed SVM method, its performance is
compared with the multi layer perceptron neural network (MLPNN).
Studies show that the SVM gives faster and more accurate results for
dynamic voltage collapse prediction compared with the MLPNN.
Keywor ds —Dynamic voltage collapse, prediction, artificial
neural network, support vector machines
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