A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks.

Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1-of- encoding ( the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks.Funding for open access charge: Universidad de Málaga / CBU

ED-Scorbot: A Robotic test-bed Framework for FPGA-based Neuromorphic systems

Neuromorphic engineering is a growing and promising discipline nowadays. Neuro-inspiration and brain understanding applied to solve engineering problems is boosting new architectures, solutions and products today. The biological brain and neural systems process information at relatively low speeds through small components, called neurons, and it is impressive how they connect each other to construct complex architectures to solve in a quasi-instantaneous way visual and audio processing tasks, object detection and tracking, target approximation, grasping…, etc., with very low power. Neuromorphs are beginning to be very promising for a new era in the development of new sensors, processors, robots and software systems that mimic these biological systems. The event-driven Scorbot (EDScorbot) is a robotic arm plus a set of FPGA / microcontroller’s boards and a library of FPGA logic joined in a completely event-based framework (spike-based) from the sensors to the actuators. It is located in Seville (University of Seville) and can be used remotely. Spike-based commands, through neuro-inspired motor controllers, can be sent to the robot after visual processing object detection and tracking for grasping or manipulation, after complex visual and audio-visual sensory fusion, or after performing a learning task. Thanks to the cascade FPGA architecture through the Address-Event-Representation (AER) bus, supported by specialized boards, resources for algorithms implementation are not limited.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

patrimonio intelectual

Actas de congresoLas VI Jornadas se realizaron con la exposición de ponencias que se incluyeron en cuatro ejes temáticos, que se desarrollaron de modo sucesivo para facilitar la asistencia, el intercambio y el debate, distribuidos en tres jornadas. Los ejes temáticos abordados fueron: 1. La enseñanza como proyecto de investigación. Recursos de enseñanza-aprendizaje como mejoras de la calidad educativa. 2. La experimentación como proyecto de investigación. Del ensayo a la aplicabilidad territorial, urbana, arquitectónica y de diseño industrial. 3. Tiempo y espacio como proyecto de investigación. Sentido, destino y usos del patrimonio construido y simbólico. 4. Idea constructiva, formulación y ejecución como proyecto de investigación. Búsqueda y elaboración de resultados que conforman los proyectos de la arquitectura y el diseño