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Thermal comfort is the condition in which a person feels satisfaction with the thermal environment through a subjective evaluation. In this work, a compact and efficient estimation of thermal comfort perception by human subjects is performed using a constructive neurocomputational model trained with data generated in controlled conditions with 49 volunteers giving 705 different scenarios, allowing, thanks to the versatility of the model, an interpretable and simple resulting function facilitating an easy handling of the results by people from different fields. The results have been compared with two of the most used standard methods for modelling thermal comfort: Fanger and COMFA models, and they show an improvement in terms of accuracy and mean square error both in a binary decision scenario (comfort or not) as well as for a discrete decision-making case in which different thermal comfort regions are considered. The flexibility of the neural model permits the incorporation of extra subject-related variables that increases further the thermal comfort estimation and, also, permits the implementation of the model in distributed and low cost/low consumption systems.

The main objective of this work has been to implement a model to find equidistant real roots using a Successive Adaptive Linear Neural Modeling which uses two approaches: a Self Organized Map (SOM) and an Adaptative Linear Neuron (Adaline). A SOM model has been used with a new neighborhood function Λ, and a physical distance β with which the task is divided in sub-processes reducing the complexity of the task because the SOM model can delimited the areas where a single root exist. Then, through a successive approach, it is applied an Feed-forward neural model with a learning process base on Adaline neuron with pocket in each pair of regions for finding the real root values with a reduced precision. Finally, several experiments were done consider CPU time, relative error, distance between the roots and polynomial degrees. The results show that the time complexity grows in a linear or logarithmic way. Also, the error does not increase in a higher rate than the degree of polynomial or the root distance.

Defining a relationship among companies that belong to the BMEX group in order to provide investors with information, is a help to minimize the risk at the moment of investing. Using data taken from Yahoo finances and BMEX website, a SOM neural network was used to study the daily data of all companies, which belong to the IBEX35 and the Latibex indexes. Companies which are part of the IBEX35, and appear closer between them in the SOM mesh, were compared in profitable terms showing that eminently there exist economic and business line relationship between them. The opposite happened companies selected randomly from the IBEX35 group in some specific cases. Likewise, the companies of Latibex group, were joined to IBEX35 companies to compare a entire year evolution between them. In fact, demonstrating that the model proposes definitely find and shows associations between companies that are near enough, and which belongs to a determinant index in a stock market environment.

A two-dimensional Ising model with nearest-neighbors ferromagnetic interactions is implemented in a Field Programmable Gate Array (FPGA) board.Extensive Monte Carlo simulations were carried out using an efficient hardware representation of individual spins and a combined global-local LFSR random number generator. Consistent results regarding the descriptive properties of magnetic systems, like energy, magnetization and susceptibility are obtained while a speed-up factor of approximately 6 times is achieved in comparison to previous FPGA-based published works and almost $10^4$ times in comparison to a standard CPU simulation. A detailed description of the logic design used is given together with a careful analysis of the quality of the random number generator used. The obtained results confirm the potential of FPGAs for analyzing the statistical mechanics of magnetic systems., 19 pages, 10 figures

The well-known backpropagation learning algorithm is implemented in a field-programmable gate array (FPGA) board and a microcontroller, focusing in obtaining efficient implementations in terms of a resource usage and computational speed. The algorithm was implemented in both cases using a training/validation/testing scheme in order to avoid overfitting problems. For the case of the FPGA implementation, a new neuron representation that reduces drastically the resource usage was introduced by combining the input and first hidden layer units in a single module. Further, a time-division multiplexing scheme was implemented for carrying out product computations taking advantage of the built-in digital signal processor cores. In both implementations, the floating-point data type representation normally used in a personal computer (PC) has been changed to a more efficient one based on a fixed-point scheme, reducing system memory variable usage and leading to an increase in computation speed. The results show that the modifications proposed produced a clear increase in computation speed in comparison with the standard PC-based implementation, demonstrating the usefulness of the intrinsic parallelism of FPGAs in neurocomputational tasks and the suitability of both implementations of the algorithm for its application to the real world problems.

The efficient implementation of artificial neural networks in FPGA boards requires tackling several issues that strongly affect the final result. One of these issues is the computation of the neuron's activation function. In this work, a detailed analysis of the FPGA implementations of the Sigmoid and Exponential functions is carried out, in a approach combining a lookup table with a linear interpolation procedure. Further, to optimize board resources utilization, a time division multiplexing of the multiplier attached to the neurons was used. The results are evaluated in terms of the absolute and relative errors obtained and also through measuring a quality factor and the resource utilization, showing a clear improvement in relationship to previously published works.