Francisco Mugica

Applying Data Mining Techniques to e-Learning Problems

This chapter aims to provide an up-to-date snapshot of the current state of research and applications of Data Mining methods in e-learning. The cross-fertilization of both areas is still in its infancy, and even academic references are scarce on the ground, although some leading education-related publications are already beginning to pay attention to this new field. In order to offer a reasonable organization of the available bibliographic information according to different criteria, firstly, and from the Data Mining practitioner point of view, references are organized according to the type of modeling techniques used, which include: Neural Networks, Genetic Algorithms, Clustering and Visualization Methods, Fuzzy Logic, Intelligent agents, and Inductive Reasoning, amongst others. From the same point of view, the information is organized according to the type of Data Mining problem dealt with: clustering, classification, prediction, etc.

Combined qualitative/quantitative simulation models of continuous-time processes using fuzzy inductive reasoning techniques

Abstract A new mixed quantitative and qualitative simulation methodology based on fuzzy inductive reasoning is presented. The feasibility of this methodology is demonstrated by means of a simple hydraulic control system. The mechanical and electrical parts of the control system are modeled using differential equations, whereas the hydraulic part is modeled using fuzzy inductive reasoning. The technique is described in detail in the first part of this paper. The example is shown in the second part of the paper. The mixed quantitative and qualitative model is simulated in ACSL, and the simulation results are compared with those obtained from a fully quantitative model. The example was chosen as a simple to describe, yet numerically demanding process whose sole purpose is to prove the concept. Several practical applications of this mixed modeling technique are mentioned in the paper. but their realization has not yet been completed

Modeling and Simulation of the Central Nervous System Control with Generic Fuzzy Models

The analysis of the human cardiovascular system by means of modeling and simulation methodologies is of relevance from a medical point of view because it allows doctors to acquire a better understanding of cardiovascular physiology, offer more accurate diagnostics, and select better suited therapies. The cardiovascular system is composed of the hemodynamical system and the central nervous system (CNS). In this work, two generic models of the CNS for patients with coronary diseases are identified by means of the fuzzy inductive reasoning (FIR) methodology. One of the models is generic only in its structure, whereas the other one is a fully generic model. It is very useful for doctors to have available a generic CNS model for a group of patients with common characteristics because this model can be used to predict the future behavior of new patients with the same characteristics.

Fuzzy inductive reasoning: a consolidated approach to data-driven construction of complex dynamical systems

Fuzzy inductive reasoning (FIR) is a modelling and simulation methodology derived from the General Systems Problem Solver. It compares favourably with other soft computing methodologies, such as neural networks, genetic or neuro-fuzzy systems, and with hard computing methodologies, such as AR, ARIMA, or NARMAX, when it is used to predict future behaviour of different kinds of systems. This paper contains an overview of the FIR methodology, its historical background, and its evolution.

Local maximum ozone concentration prediction using soft computing methodologies

The prediction of ozone levels is an important task because this toxic gas can produce harmful effects to the population health especially of children. This article describes the application of the Fuzzy Inductive Reasoning methodology and a Recurrent Neural Network (RNN) approach, the Long Short Term Memory (LSTM) architecture, to a signal forecasting task in an environmental domain. More specifically, we have applied FIR and LSTM to the prediction of maximum ozone(O3) concentrations in the East Austrian region. In this article the results of FIR and LSTM on this task are compared with those obtained previously using other types of neural networks (Multilayer Perceptrons (MLPs), Elman Networks (ENs) and Modified Elman Networks (MENs)). The performance of the best LSTM networks inferred are equivalent to the best FIR models identified and both are slightly better than the other Neural Networks studied (MENs, ENs and MLPs, in decreasing order of performance). Cross validation tests are included in this research in order to study more deeply the accuracy of the FIR models and to extract as much information as possible from the available data.

Short-term electric load forecasting using computational intelligence methods

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out, using a set of three time series from electricity consumption in the real-world domain, on different forecasting horizons.

Simulation of heat and humidity budgets of Biosphere 2 without air conditioning

Abstract The main goal of this study was the development of a dynamic model that represents the thermal behavior of the complex ecological system of Biosphere 2, Nes. Tucson, AZ, USA. In this paper, a model that captures the thermal behavior of the ecological system in a non-controlled (i.e. passive) environment is presented. The bond graph methodology was used for modeling this highly complex system. The object-oriented nature of the bond graph approach enables the modeler to keep conceptually separated aspects of knowledge about the system’s comportment isolated from each other. Thereby, the individual modeling entities remain small and manageable. This makes it easier for the modeler to properly debug and validate individual models. Uniform power-flow interfaces between all bond graph models ensure energy conservation at the connections between the individual models, and support the modeler in validating the interconnected bond graph model of the overall system. Although plausible simulation results are presented at the end of this paper, no true simulation verification could be made, because the real system has never, since its completion, been allowed to be operated in a purely passive mode, i.e. without its air handlers, as in fact, such an experiment would kill most of the biomes inside Biosphere 2. Yet, simulation runs of the passive system are meaningful for model validation purposes. The control systems that operate the air handlers reduce the sensitivity of the simulation output to modeling errors, and may, in fact, not only correct for Tucson’s hot desert climate, but also for temperature deviations caused by an incorrect mathematical description of the system thermodynamics.

PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques

In this work, a fault diagnosis methodology termed VisualBlock-Fuzzy Inductive Reasoning, i.e. VisualBlock-FIR, based on fuzzy and pattern recognition approaches is presented and applied to PEM fuel cell power systems. The innovation of this methodology is based on the hybridization of an artificial intelligence methodology that combines fuzzy approaches with well known pattern recognition techniques. To illustrate the potentiality of VisualBlock-FIR, a non-linear fuel cell simulator that has been proposed in the literature is employed. This simulator includes a set of five fault scenarios with some of the most frequent faults in fuel cell systems. The fault detection and identification results obtained for these scenarios are presented in this paper. It is remarkable that the proposed methodology compares favorably to the model-based methodology based on computing residuals while detecting and identifying all the proposed faults much more rapidly. Moreover, the robustness of the hybrid fault diagnosis methodology is also studied, showing good behavior even with a level of noise of 20dB.

Inductive Reasoning Supports the Design of Fuzzy Controllers

In this article, a new systematic design methodology for fuzzy controllers is presented. For any desired plant output, it is possible to find off-line the optimal plant input that will produce a plan output that is as close as possible to the desired one. However, this constitutes an open-loop design. In this article, a new methodology is introduced that allows computing a signal on-line that is close to the optimal plant input as a function of system inputs and plant outputs. To this end, an inductive reasoning model is created that estimates the optimal plant input from given system inputs and plant outputs. The inductive reasoning model can be interpreted and realized as a fuzzy controller. Thereby, a large portion of the controller is realized through feedback, and the previous open-loop design is converted to an equivalent and more robust closed-loop design. The inductive reasoning technique is described in detail in the first part of this article. An example is shown in the second part of the article to demonstrate the validity of the chosen approach.

Small-Particle Pollution Modeling Using Fuzzy Approaches

Air pollution caused by small particles is a major public health problem in many cities of the world. One of the most contaminated cities is Mexico City. The fact that it is located in a volcanic crater surrounded by mountains helps thermal inversion and imply a huge pollution problem by trapping a thick layer of smog that float over the city. Modeling air pollution is a political and administrative important issue due to the fact that the prediction of critical events should guide decision making. The need for countermeasures against such episodes requires predicting with accuracy and in advance relevant indicators of air pollution, such are particles smaller than 2.5 microns (PM2.5). In this work two different fuzzy approaches for modeling PM2.5 concentrations in Mexico City metropolitan area are compared with respect the simple persistence method.