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Artificial Intelligence Research and Development

  • C. Angulo and L. Godo Eds. Artificial Intelligence Research and Development, Volume 163 Frontiers in Artificial Intelligence and Applications, October 2007, 450 pp., hardcover. ISBN: 978-1-58603-798-7

  • Sinopsis. "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed...." Last year the 50th anniversary of the Dartmouth AI project proposal by McCarthy, Minsky, Rochester and Shannon was celebrated. Years later, and following similar traditions of a number of AI associations, a call was launched in 1997 by the Catalan Association for Artificial Intelligence (ACIA) to organize an annual conference to promote synergies in the research community of its influence, the seeder for the 1st Catalan Conference on Artificial Intelligence (CCIA'98) which took place in Tarragona on October 1998. The editors of this book are very glad to celebrate the 10th anniversary of the International Conference of the ACIA (CCIA'07) in Sant Julià de Lòria (Andorra), October 25-26th, 2007. The good health of the Catalan AI community and its influence area is witnessed by the representative selection of papers gathered in this book and presented at CCIA'07. The book is organized according to the different areas in which the papers were distributed for their presentation during the conference, namely: Constraint Satisfaction, Agents, Data Processing, Case-Based Reasoning, Computer Vision, Natural Language Processing, Uncertainty and Fuzziness, Robotics, and Applications. The editors believe that all the papers collected in this volume can be of interest to any computer scientist or engineer interested in AI.


Tecnología de Sistemas de Control

  • Cecilio Angulo Bahón, Cristóbal Raya Giner, Tecnología de Sistemas de Control, EdicionsUPC, 2004 (183 pág.) ISBN: 84-830-1778-4.

  • Sinopsis. Tecnología de sistemas de control presenta la ingeniería de control que enlaza los conocimientos analíticos de la regulación con los dispositivos en el mercado para sintonizar de forma empírica, basándose en especificaciones analíticas de control. Siguiendo el esquema básico de diseño de los sistemas de control, se utiliza el entorno MATLAB para profundizar en los diferentes elementos de regulación y actuación mediante ejemplos y ejercicios prácticos. Además, se aporta información relacionada con la implementación práctica de estructuras de regulación mediante dispositivos de control, y se explica la normativa y notación estándares para facilitar la lectura de las especificaciones de los componentes. Gracias a este compromiso analítico-empírico, la presente obra resulta especialmente indicada para estudiantes de ingeniería y jóvenes graduados que buscan la sintonía entre el discurso académico de la teoría de control y su implantación sobre los lazos de control de las plantas industriales.

  • Índice.

    1. Introducción a la tecnología de control
    2. Elementos lineales de control
    3. Modificaciones del algoritmo PID
    4. Normativa de representación
    5. Modelos de proceso
    6. Diseño de controladores PID. Sintonía de parámetros
    7. Componentes de control no lineal
    8. Esructuras de regulación y control

Aprendizaje con Máquinas Núcleo en Entornos de Multiclasificación

  • Cecilio Angulo, Aprendizaje con Máquinas Núcleo en Entornos de Multiclasificación, UPC, 2001 (176 pág.) ISBN: 84-699-5513-6.

  • Abstract. The property of generalization of a learning machine, i.e. its capacity to emit a correct answer on a new similar input to those with wich it has been trained, is the basic behavior looked for in the supervised connexionists systems and it serves as justification in the selection of the inductive principles and the type of learning structures to ellaborate the present study.
    The penalty is one of these principles that favor at theoretical level the generalization, on which a method of direct calculation of the regularization matrix when a second degree differential operator is used like stabilizer, indeed that diminishing the convexity degree of the solution function, avoiding therefore the iterative process of calculation of the Hessian matrix, has been developed and fixing the type of kernel to be used.
    Links between regularization and the structural risk minimization principle as well as the excellent theoretical characteristics shown by this last principle working, by definition, on finite data sets and expanding their solution on a small number of kernels, have taken to move the center of study of numerous investigators towards the support vector machines, their procedural materialization. In this context, a machine that allows to extend of natural form the binary behavior of these maximum margin ker-nel machines on classification problems towards an agreed ternary solution with the geometric structure of the data has been developed, in special in the habitual situations of output spaces having more than two classes.
    The use of the new architecture, named K-SVCR, in multiclassification problems is more suitable than the standard reductions from multiclass problems on biclass machines in tree or parallel structures, since each di-chotomie node considers all the training space and force to the hyperplane of separation to consider the geometric structure of the training patterns.
    In special, the robustness of the new method is demostrated on failures in the predictions of some of its working nodes when a special type of combination of these answers is considered.
    The new architecture of multiclassification has been modified later to be implemented on a classification problem with independent characteristics, the ordenation or learning of preferences problem. Their benefits are evaluated on a financial application in the determination of credit risks.
    Finally, an application of categorization in waste water plant scenes, where the temporality affects, also serves like operation example.

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