Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%

Adaptive control and neural network control of nonlinear discrete-time systems

Ph.DDOCTOR OF PHILOSOPH

Implementing Industry 4.0 in SMEs

This open access book addresses the practical challenges that Industry 4.0 presents for SMEs. While large companies are already responding to the changes resulting from the fourth industrial revolution , small businesses are in danger of falling behind due to the lack of examples, best practices and established methods and tools. Following on from the publication of the previous book ‘Industry 4.0 for SMEs: Challenges, Opportunities and Requirements’, the authors offer in this new book innovative results from research on smart manufacturing, smart logistics and managerial models for SMEs. Based on a large scale EU-funded research project involving seven academic institutions from three continents and a network of over fifty small and medium sized enterprises, the book reveals the methods and tools required to support the successful implementation of Industry 4.0 along with practical examples

Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería

El propósito de esta Tesis Doctoral es proponer una alternativa viable a la aproximación de modelos y procesos en el ámbito científico y, más concretamente, en aplicaciones complejas de bioingeniería, en las cuales es imposible o muy costoso encontrar una relación directa entre las señales de entrada y de salida mediante modelos matemáticos sencillos o aproximaciones estadísticas. Del mismo modo, es interesante lograr una compactación de los datos que necesita un modelo para conseguir una predicción o clasificación en un tiempo y con un coste de implementación mínimos. Un modelo puede ser simplificado en gran medida al reducir el número de entradas o realizar operaciones matemáticas sobre éstas para transformarlas en nuevas variables. En muchos problemas de regresión (aproximación de funciones), clasificación y optimización, en general se hace uso de las nuevas metodologías basadas en la inteligencia artificial. La inteligencia artificial es una rama de las ciencias de la computación que busca automatizar la capacidad de un sistema para responder a los estímulos que recibe y proponer salidas adecuadas y racionales. Esto se produce gracias a un proceso de aprendizaje, mediante el cual se presentan ciertas muestras o �ejemplos� al modelo y sus correspondientes salidas y éste aprende a proponer las salidas correspondientes a nuevos estímulos que no ha visto previamente. Esto se denomina aprendizaje supervisado. También puede darse el caso de que tal modelo asocie las entradas con características similares entre sí para obtener una clasificación de las muestras de entrada sin necesidad de un patrón de salida. Este modelo de aprendizaje se denomina no supervisado. El principal exponente de la aplicación de la inteligencia artificial para aproximación de funciones y clasificación son las redes neuronales artificiales. Se trata de modelos que han demostrado sobradamente sus ventajas en el ámbito del modelado estadístico y de la predicción frente a otros métodos clásicos. NMateo Jiménez, F. (2012). Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16702Palanci