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The Echo State Network (ESN) is a specific recurrent network, which has gained popularity during the last years. The model has a recurrent network named reservoir, that is fixed during the learning process. The reservoir is used for transforming the input space in a larger space. A fundamental property that provokes an impact on the model accuracy is the Echo State Property (ESP). There are two main...
Several works have been applied non-temporal classification techniques in the Human Activity Recognition area. Instead of that, we present an approach for modelling the human activities using a temporal learning tool. Here, the activities are considered as time-dependent events, and we use a temporal learning method for their classification. We employ a well-known learning tool named Echo State Network...
The article presents an application of hidden Markov models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the variant surface glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These...
This paper presents an analysis of the impact of the parameters of an Echo State Network (ESN) on its performance. In particular, we are interested on the parameter behaviour when the model is used for forecasting pseudo-periodic time series. According previous literature, the spectral radius of the hidden-hidden weight matrix of the ESN is a relevant parameter on the model performance. It impacts...
We present an approach for emotion recognition using information of the pupil. In last years, the pupil variables have been used as an assessment of emotional arousal. In this article, we generate signals of pupil size and gaze position monitored during image viewing. The emotions are provoked by visual stimuli of colored images. Those images were taken from the International Affective Picture System...
In last years -- especially due to the development of telecommunications -- fairness modelling has received a strong attention. This article presents an approach for categorizing unknown relations according to their "closeness" to known relations. We consider as reference relations, the well-known: Pareto dominance, Leximin and Proportional fairness relation. We simulate each relation generating...
This article introduces a metaheuristic approach to solve a variation of the well-known Vehicle Routing Problem (VRP). We present a solution for the Multi-Trip VRP with Time Windows and Heterogeneous Fleet. We add constraints to the original VRP concerning the time and the customer supply. Time constraints concerns the time windows on each customer and time horizon within which customers must be satisfied...
This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the hidden-hidden weights fixed in the learning process. The recurrent part of the network stores the input information in internal states of the network. Another structure...
Today renewable energy sources are integral part of energy mix in most of countries in the world. Carbon reduction issues and other ecological activity provide a wide possibility to progressive increase of installed capacity of renewable energy sources. Huge distribution of instable renewable energy sources like wind and photovoltaic plants brings new tasks in power system control and power system...
At the beginning of the 2000s was introduced the Echo State Network model (ESN). The model has been successfully used in temporal learning tasks. In spite of its success in practical applications, the model can present some stability problems when the parameters are not well initialized. The stability of the model is associated with the spectrum of the weight matrix. To compute the spectra is an expensive...
This paper investigates the estimation of a real time-series benchmark: the solar irradiance forcasting. The global solar irradiance is an important variable in the production of renewable energy sources. These variable is very unstable and hard to be predicted. For the prediction, we use two new models for time-series modeling: Echo State Queueing Networks and Differential polynomial Neural Networks...
Since the early 1990s, Random Neural Networks (RNNs) have gained importance in the Neural Networks and Queueing Networks communities. RNNs are inspired by biological neural networks and they are also an extension of open Jackson's networks in Queueing Theory. In 1993, a learning algorithm of gradient type was introduced in order to use RNNs in supervised learning tasks. This method considers only...
In this paper we present a general procedure to use Bagging techniques for time series processing and forecasting problems Bagging is one of the most used techniques for combining several predictors in order to produce a highly accurate method. The method uses bootstrap replications of the original training set and for each replicate sample one predictor is generated. After that the method combines...
In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has grown rapidly due to its success in solving learning...
In the last years a new approach for designing and training artificial Recurrent Neural Network (RNN) have been investigated under the name of Reservoir Computing (RC). One important model in the field of RC has been developed under the name of Echo State Networks (ESNs). Traditionally, an ESN uses a RNN with random untrained parameters called the reservoir. The Self-Organizing Map (SOM) and the Scale...
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