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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.

In this paper, a new self-organizing artificial neural network called growing hierarchical neural forest (GHNF) is proposed. The GHNF is a hierarchical model based on the growing neural forest, which is a tree-based model that learns a set of trees (forest) instead of a general graph so that the forest can grow in size. This way, the GHNF faces three important limitations regarding the self-organizing map: fixed size, fixed topology, and lack of hierarchical representation for input data. Hence, the GHNF is especially amenable to datasets containing clusters where each cluster has a hierarchical structure since each tree of the GHNF forest can adapt to one of the clusters. Experimental results show the goodness of our proposal in terms of self-organization and clustering capabilities. In particular, it has been applied to text mining of tweets as a typical exploratory data analysis application, where a hierarchical representation of concepts present in tweets has been obtained. Moreover, it has been applied to foreground detection in video sequences, outperforming several methods specialized in foreground detection.

In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantization of the original image. Experimental results confirm the color quantization capabilities of this approach.

C-Mantec neural network constructive algorithm Ortega (C-Mantec neural network algorithm implementation on MATLAB. https://github.com/IvanGGomez/CmantecPaco, 2015) creates very compact architectures with generalization capabilities similar to feed-forward networks trained by the well-known back-propagation algorithm. Nevertheless, constructive algorithms suffer much from the problem of overfitting, and thus, in this work the learning procedure is first analyzed for networks created by this algorithm with the aim of trying to understand the training dynamics that will permit optimization possibilities. Secondly, several optimization strategies are analyzed for the position of class separating hyperplanes, and the results analyzed on a set of public domain benchmark data sets. The results indicate that with these modifications a small increase in prediction accuracy of C-Mantec can be obtained but in general this was not better when compared to a standard support vector machine, except in some cases when a mixed strategy is used.

Since the origins of artificial neural network research, many models of feedforward networks have been proposed. This paper presents an algorithm which adapts the shape of the activation function to the training data, so that it is learned along with the connection weights. The activation function is interpreted as a piecewise polynomial approximation to the distribution function of the argument of the activation function. An online learning procedure is given, and it is formally proved that it makes the training error decrease or stay the same except for extreme cases. Moreover, the model is computationally simpler than standard feedforward networks, so that it is suitable for implementation on FPGAs and microcontrollers. However, our present proposal is limited to two-layer, one-output-neuron architectures due to the lack of differentiability of the learned activation functions with respect to the node locations. Experimental results are provided, which show the performance of the proposal algorithm for classification and regression applications.

Most clustering algorithms are designed to minimize a distortion measure which quantifies how far the elements of the clusters are from their respective centroids. The assessment of the results is often carried out with the help of cluster quality measures which take into account the compactness and separation of the clusters. However, these measures are not amenable to optimization because they are not differentiable with respect to the centroids even for a given set of clusters. Here we propose a differentiable cluster quality measure, and an associated clustering algorithm to optimize it. It turns out that the standard k-means algorithm is a special case of our method. Experimental results are reported with both synthetic and real datasets, which demonstrate the performance of our approach with respect to several standard quantitative measures.

Recent advances in FPGA technology have permitted the implementation of neurocomputational models, making them an interesting alternative to standard PCs in order to speed up the computations involved taking advantage of the intrinsic FPGA parallelism. In this work, we analyse and compare the FPGA implementation of two neural network learning algorithms: the standard and well known Back-Propagation algorithm and C-Mantec, a constructive neural network algorithm that generates compact one hidden layer architectures with good predictive capabilities. One of the main differences between both algorithms is the fact that while Back-Propagation needs a predefined architecture, C-Mantec constructs its network while learning the input patterns. Several aspects of the FPGA implementation of both algorithms are analyzed, focusing in features like logic and memory resources needed, transfer function implementation, computation time, etc. The advantages and disadvantages of both methods in relationship to their hardware implementations are discussed.

A low cost smart motion detector is presented.It is based on the Arduino DUE microcontroller.The software architecture employs a fixed point arithmetic paradigm.The self-organizing map neural network is implemented on chip.The performance is substantially higher than that of the traditional detector. Most current approaches to computer vision are based on expensive, high performance hardware to meet the heavy computational requirements of the employed algorithms. These system architectures are severely limited in their practical application due to financial and technical limitations. In this work a different strategy is used, namely the development of an inexpensive and easy to deploy computer vision system for motion detection. This is achieved by three means. First of all, an affordable and flexible hardware platform is employed. Secondly, the motion detection algorithm is specifically tailored to involve a very small computational load. Thirdly, a fixed point programming paradigm is followed in implementing the system so as to further reduce the computational requirements. The proposed system is experimentally compared to the standard motion detector for a wide range of benchmark videos. The reported results indicate that our proposal attains substantially better performance, while it remains affordable and easy to install in practice.

Learning math is important for the academic life of students: the development of mathematical skills is influenced by different characteristics of students such as geographical position, economic level, parents’ education, achievement level, teacher objectives, social level, use of information and communication technologies by teachers, learner motivation, gender, age, preferences for playing video games, and the school year of the students. In this work, these previously mentioned characteristics were considered as the attributes (inputs) of a multilayer neural network that uses a backpropagation algorithm to predict the percentage of improvement in mathematics through a 2D mathematical video game that was developed to allow the children to practice addition and subtraction operations. After applying the neural model, using the twelve attributes mentioned before and the backpropagation algorithm, there was a network of one layer with ten neurons and another network of two layers with 5 neurons in the first layer and 20 neurons in the second layer. Both architectures produced a mean squared error smaller than 0.0069 in the prediction of the student’s percentage of improvement in mathematics, being the best configurations found in this study for the neural model. These results lead to the conclusion that we are able to predict the percentage of improvement in math that the students could achieve after playing the game, and therefore, claiming if the video game is recommendable or not for certain students.

A portable an distributed expert system has been implemented in order to carry out voice and speech recognition of different people. The device consists of an open source microcontroller connected to a microphone that would be able to instantly recognize words and people, and could be distributed as a real time device in the security field. Two different methods of artificial neural network have been performed to implement the system: the first one has been the Continuous Hopfield Network to store and recover patterns previously stored, in order to know the word used to communicate with the system, the second one has been the Backpropagation Algorithm used to recognize between different speakers. However prior to performing these methods, it has been necessary to pre-process the information so that it can be computed in this type of device, transforming sound files into a set of useful data using a Fast Fourier Transform. The results obtained have been satisfactory with an accuracy above 80% for words recovery with a white noise of 40% of the signal and above 80% for recognizing a person with a new words introduced.

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.

Competitive majority network trained by error correction (C-Mantec), a recently proposed constructive neural network algorithm that generates very compact architectures with good generalization capabilities, is implemented in a field programmable gate array (FPGA). A clear difference with most of the existing neural network implementations (most of them based on the use of the backpropagation algorithm) is that the C-Mantec automatically generates an adequate neural architecture while the training of the data is performed. All the steps involved in the implementation, including the on-chip learning phase, are fully described and a deep analysis of the results is carried on using the two sets of benchmark problems. The results show a clear increase in the computation speed in comparison to the standard personal computer (PC)-based implementation, demonstrating the usefulness of the intrinsic parallelism of FPGAs in the neurocomputational tasks and the suitability of the hardware version of the C-Mantec algorithm for its application to real-world problems.

The techniques currently developed for updating software in sensor nodes located in changing environments require usually the use of reprogramming procedures, which clearly increments the costs in terms of time and energy consumption. This work presents an alternative to the traditional reprogramming approach based on an on-chip learning scheme in order to adapt the node behaviour to the environment conditions. The proposed learning scheme is based on C-Mantec, a novel constructive neural network algorithm especially suitable for microcontroller implementations as it generates very compact size architectures. The Arduino UNO board was selected to implement this learning algorithm as it is a popular, economic and efficient open source single-board microcontroller. C-Mantec has been successfully implemented in a microcontroller board by adapting it in order to overcome the limitations imposed by the limited resources of memory and computing speed of the hardware device. Also, this work brings an in-depth analysis of the solutions adopted to overcome hardware resource limitations in the learning algorithm implementation (e.g., data type), together with an efficiency assessment of this approach when the algorithm is tested on a set of circuit design benchmark functions. Finally, the utility, efficiency and versatility of the system is tested in three different-nature case studies in which the environmental conditions change its behaviour over time.

Scheduling problems are very hard computational tasks with several applications in multitude of domains. In this work we solve a practical problem motivated by a real industry situation, in which we apply a genetic algorithm for finding an acceptable solution in a very short time interval. The main novelty introduced in this work is the use of a priority based chromosome codification that determines the precedence of a task with respect to other ones, permitting to introduce in a very simple way all problem constraints, including setup costs and workforce availability. Results show the suitability of the approach, obtaining real time solutions for tasks with up to 50 products.

Thermal comfort conditions are important for the normal development of human tasks, and as such they have been analyzed in the context of several areas including human physiology, ergonomics, heating and cooling systems, architectural design, etc. In this work, we analyze the estimation of the thermal comfort perception by human subjects using a neurocomputational model based on the C-Mantec constructive neural network architecture, comparing it with two standard methods for modeling thermal comfort: Fanger and COMFA models. The results indicate a significative advantage of C-Mantec in terms of the predictive accuracy obtained, consider also that the flexibility of the neural model would permit the introduction of extra variables that can increase further the thermal comfort estimation.

In recent years predictive models based on Deep Learning strategies have achieved enormous success in several domains including pattern recognition tasks, language translation, software design, etc. Deep learning uses a combination of techniques to achieve its prediction accuracy, but essentially all existing approaches are based on multi-layer neural networks with deep architectures, i.e., several layers of processing units containing a large number of neurons. As the simulation of large networks requires heavy computational power, GPUs and cluster based computation strategies have been successfully used. In this work, a layer multiplexing scheme is presented in order to permit the simulation of deep neural networks in FPGA boards. As a demonstration of the usefulness of the scheme deep architectures trained by the classical Back-Propagation algorithm are simulated on FPGA boards and compared to standard implementations, showing the advantages in computation speed of the proposed scheme.

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.

Recent advances in FPGA technology have permitted the implementation of neurocomputational models, making them an interesting alternative to standard PCs in order to speed up the computations involved taking advantage of the intrinsic FPGA parallelism. In this work, we analyse and compare the FPGA implementation of two neural network learning algorithms: the standard Back-Propagation algorithm and C-Mantec, a constructive neural network algorithm that generates compact one hidden layer architectures. One of the main differences between both algorithms is the fact that while Back-Propagation needs a predefined architecture, C-Mantec constructs its network while learning the input patterns. Several aspects of the FPGA implementation of both algorithms are analysed, focusing in features like logic and memory resources needed, transfer function implementation, computation time, etc. Advantages and disadvantages of both methods are discussed in the context of their application to benchmark 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.

C-Mantec is a recently introduced constructive algorithm that generates compact neural architectures with good generalization abilities. Nevertheless, it produces a discrete output value and this might be a drawback in certain situations. We propose in this work two approaches in order to obtain a continuous output network such as the output can be interpreted as the probability of a given pattern to belong to one of the output classes. The CC-Mantec approach utilizes a committee strategy and the results obtained both with the XOR Boolean function and with a set of benchmark functions shows the suitability of the approach, as an improvement over the standard C-Mantec algorithm is obtained in almost all cases.

A recently proposed constructive neural network algorithm, named C-Mantec, is fully implemented in a Arduino board. The C-Mantec algorithm generates very compact size neural architectures with good prediction abilities, and thus the board can be potentially used to learn on-site sensed data without needing to transmit information to a central control unit. An analysis of the more difficult steps of the implementation is detailed, and a test is carried out on a set of benchmark functions normally used in circuit design to show the correct functioning of the implementation.