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In some other articles in this blog, I have already written about complex time series, recurrence quantification analysis (RQA) and neural networks. In this series of articles, I will discuss some points to take into account when combining the use of these two tools to identify patterns in complex series, such as detecting anomalies in electrocardiograms or electroencephalograms.

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With this article I conclude the series dedicated to the application of genetic algorithms to the design of neural networks. I will explain the most relevant code of the sample application given with these articles, mainly the classes dedicated to the genes management and the selection process. You can find more information in the previous articles of the series.

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In this second article on the application of evolutionary algorithms to the optimization of the design of neural networks, I am going to provide a small sample application that allows you to build and train networks, in addition to using this type of algorithms to find the best configuration for a given data set. The application allows generating artificial test data, and I provide the source code for you to be able to modify it, as you want.

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When we try to apply a neural network to a given problem, finding the most suitable topology for it can be a tedious trial and error task, as well as end up producing a poorly optimized network. To automate this process, we can draw on evolutionary algorithms, inspired in the natural selection of living organisms, which can greatly facilitate our job.

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This is the last article of the series dedicated to the WinCA application. This application allows the edition and to execution of cellular automata. To finish, I will review the code that implements the automaton itself, using all the classes and interfaces explained in the previous articles.

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I continue explaining the basics of the WinCA application code, dedicated to the designing and execution of cellular automata. This time I will tell you about the implementation of the cells and other auxiliary classes needed to build and execute automata.

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We continue with the series dedicated to cellular automata and the WinCA application, dedicated to build and executing them. In this article I will explain the code related to the expression system that allows establishing the conditions to change from one state to another.

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I continue to comment on the source code of the WinCA program, dedicated to cellular automata. In this article I will explain the interfaces and classes with which cell states are implemented and their edition. You can use these interfaces and classes as a basis to extend the application features.

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Once reviewed the operation of the WinCA application, dedicated to cellular automata, let's see how the source code is organized. In this article I will explain the properties of cell states, and how they can be extended to add new functionalities by implementing new classes with the corresponding interfaces.

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In this article, third of the series, I continue to explain the operation of the WinCA application, devoted to the construction and execution of cellular automata. This time I will show the language used to define the transitions between the different states of the automaton cells.

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This is the second article in the series about the WinCA application, dedicated to the construction and execution of cellular automata. In this article I will show how to design the diferent states that can have the automaton cells, and the properties that can be assigned to them, using the application editors.

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Cellular automata are mathematical models used to study the evolution of complex dynamic systems by simulating the interactions over time of a large number of elements, called cells. In this series of articles I will present the WinCA application, with which you can build and run this type of objects.

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WInRQA is an application dedicated to recurrence plots, a tool that is used in the analysis of recurrence of complex time series. In this article I will introduce a new tool that I have added to the program. Until now, the measures of quantification of recurrence (RQM) were obtained only from a static portion of the original series. With the new tool, we can obtain a series of measures by moving a window along the entire original series and calculating the corresponding measurements to each of these windows.

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The Iterated function systems (IFS) are a simple mathematical tool for constructing fractal sets through a series of contractive affine applications. This method was developed by M.F. Barnsley in 1985. In particular, it is useful to obtain a self-similar fractal based on iteratively applying the system of functions to any set, until arriving at a good approximation of the fractal set that constitutes the attractor of the system.

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WInRQA is an application dedicated to recurrence plots, a tool that is used in the analysis of recurrence of complex time series. In this article I will present the first extension of the application, which mainly consists of a series of tool windows that will help you to make estimates on what may be the most appropriate delay to try to reconstruct the phase space of the system attractor and select the correct embedding dimension.

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In this article I will show how, through a very simple and totally deterministic process, we can move from a stationary system to a completely random one, going through periodic and chaotic dynamics. For this, I will generate several time series with these characteristics using the program R and several packages that can help us in the analysis of them.

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A recurrence plot is a graphical tool used in the study of complex time series. Along with the plot we can also calculate a series of measures that allow us to perform a recurrence quantification analysis, or RQA. In this article I will present the WinRQA application, a tool to work with recurrence plots and RQA measures in a Windows environment.

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To conclude this series on complex time series and their characterization using graphical tools I will show you a tool called recurrence plot, which allows to obtain some measures used in the recurrence quantification analysis, or RQA for its acronym in English. The recurrence is a characteristic property of deterministic dynamical systems, and consists of that two or more states of the system are arbitrarily close after a certain period of time.

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In this new article in the series on time series with complex dynamics, I will show you a procedure to approximately reconstruct the information of a dynamic system with two or more variables from a single series, i.e. a set of data in a single dimension. What we will get from this unique series is a new one for each of the extra dimensions with which we intend to extend the model.

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In this new article of the series dedicated to the graphic characterization of complex time series I will talk about two other graphical tools that can be useful, the power spectrum of the signal, which will be obtained through the Fourier transform, and the graph of the distribution of values of the series, a simple histogram with the frequency of the different values that also can provide us information about the series dynamics.

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