Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots. Since the industrial robot is a complex nonlinear system, the traditional approximate reliability methods often produce unreliable results in analyzing its positioning accuracy. In order to study the positioning accuracy reliability of industrial robot more efficiently and accurately, a radial basis function network is used to construct the mapping relationship between the uncertain parameters and the position coordinates of the end-effector. Combining with the Monte Carlo simulation method, the positioning accuracy reliability is then evaluated. A novel hybrid learning algorithm for training radial basis function network, which integrates the clustering learning algorithm and the orthogonal least squares learning algorithm, is proposed in this article. Examples are presented to illustrate the high proficiency and reliability of the proposed method

Nonlinear moving-horizon state estimation for hardware implementation and a model predictive control application

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2021.Nesta dissertação, exploramos a aplicação de redes neurais artificiais de funções de base radial (RBFs) embutidas em hardware para estimação de estados e controle em tempo real utilizando os algoritmos de Moving-Horizon Estimation(MHE) e Model Predictive Control (MPC). Esses algoritmos foram posteriormente aproximados por RBFs e implementados em um Field Programmable Gate Array (FPGA), que tem mostrado bons resultados em termos de precisão e tempo ˜ computacional. Mostramos que a estimativa de estado usando a versão aproximada do MHE ˜ pode ser executada usando um kit em escala de laboratório de aproximadamente 500 kHz para ´ um pendulo invertido a uma taxa de clock de cerca de 110 MHz. A latência para fornecer uma estimativa pode ser reduzida ainda mais quando FPGAs com clocks mais altos são usados, pois a ˜ arquitetura da rede neural artificial e inerentemente paralela. Após uma inspeção mais detalhada, ˜ descobriu-se que era possível reduzir o custo da área de chip trocando a função de custo por uma ˜ com resultados mais facilmente representáveis. Ele poderia então utilizar uma representação em ˜ 32 bits e o modulo CORDIC poderia ser removido, usando apenas a aproximação mais simples da ˜ serie de Taylor de 2 ´ ª ordem. Em seguida, expandimos isso, investigando a ideia de usar uma única rede neural para substituir tanto o controle quanto o estimatidor de estados. Comparado a um MPC com informações completas, sua versão utilizando o MHE não teve um bom desempenho contra ˜ ruídos de saída. A princípio não foi possível aproximar o controle e a estimativa do pêndulo com um bom resultado, porem ao separar o controle em duas partes obtivemos melhores resultados. Por fim, verificamos que tal rede neural foi capaz de estabilizar o sistema de pendulo invertido, ˆ mas não de aproximar sua parte oscilante n ˜ ao linear. A solução aqui apresentada ˜ e encorajada a ser estendida para sistemas mais complexos e não lineares, uma vez que uma arquitetura com ˜ complexidade razoável é encontrada para a rede neural artificial para ser implementada.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).In this dissertation, we explore the application of radial basis functions (RBFs) artificial neural networks embedded in hardware for real-time estimation and control algorithms as the Moving- Horizon Estimation (MHE) and the Model Predictive Control (MPC). These algorithms are then approximated using RBFs and implemented in a Field Programmable Gate Array (FPGA), which has shown good results in terms of accuracy and computational time. We show that the state estimate using the approximate version of the MHE can be run using a laboratory-scale kit of approximately 500 kHz for an inverted pendulum at a clock rate of about 110 MHz. The latency to provide an estimate can be further reduced when FPGAs with higher clocks are used as the artificial neural network architecture is inherently parallel. Upon further inspection, it was found to be possible to reduce the chip area cost by switching the cost function for one with more easily representable results. It could then utilize a 32-bits representation and the CORDIC module could be removed, using instead only the simpler 2o order Taylor approximation. We then expand upon this, probing at the idea of using a single neural network to substitute both the control and state-estimation. Compared to a MPC with full information, its version utilizing the MHE did not perform well against output noises. At first, it was not possible to approximate the pendulum control and estimation with a good result, however when separating the control in two parts we gained better outcomes. Lastly, we verify that such a neural network was capable of stabilizing the inverted pendulum system, but not of approximating the non-linear swing-up part of it. The solution herein presented is encouraged to be further extended for more complex and nonlinear systems, given that an architecture is found for the artificial neural network with reasonable complexity to be implemented

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice