Carelia Gaxiola-Pacheco

JT2FISA Java Type-2 Fuzzy Inference Systems Class Library for Building Object-Oriented Intelligent Applications

This paper introduces JT2FIS, a Java Class Library for Interval Type-2 Fuzzy Inference Systems that can be used to build intelligent object-oriented applications. The architecture of the system is presented and its object-oriented design is described. We used the water temperature and flow control as a classic example to show how to use it on engineering applications. We compared the developed library with an existing Matlab® Interval Type-2 Fuzzy Toolbox and Juzzy Toolkit in order to show the advantages of the proposed application programming interface (API) features.

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi–Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.

Hybrid-Intelligent Mobile Indoor Location Using Wi-Fi Signals

Technology with situational awareness needs a lot of information of the environment to execute the correct task at the correct moment. Location of the user is typical information to achieve the goal. This work proposes a mobile application that enables the indoor location of smartphones using the potential infrastructure given by Wireless Local Area Networks. This infrastructure goes beyond GPS (Global Position System) where signal is weak or is not available for indoors. This application uses an alternative and unconventional method to indoor location using Wi-Fi RSSI fingerprinting as well as an estimation based on Type-2 fuzzy inference systems provided by the developed framework JT2FIS. Wi-Fi Fingerprinting creates a radio map of a given area based on the RSSI data from several access points (APs) and generates a set of RSSI data for a given zone location. Consequently Data Mining is required for clustering the obtained set of data and generating the structure of a Type-2 Mamdani or Takagi-Sugeno Fuzzy Inference System; thus new RSSI values are introduced to the Type-2 Fuzzy Inference System to obtain an estimation of the user zone location.

Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems☆

Abstract This paper introduces JT2FISClustering, a data mining extension for JT2FIS. JT2FIS is a Java class library for building intelligent applications. This extension is used to extract information from a data set and transform it into an Interval Type-2 Fuzzy Inference System in Java applications. Mamdani and Takagi-Sugeno Fuzzy Inference Systems can be generated using fuzzy c-means or subtractive data mining methods. We compare the outputs and performance of Matlab R versus Java in order to validate the proposed extension.

Towards A Multi-Dimensional Modelling Of Complex Social Systems Using Data Mining And Type-2 Neuro-Fuzzy System: Religious Affiliation Case Of Study.

The purpose of this paper is, to describe a work-in process for application of distributed agency methodology to multi-dimensional preference model into a complex social system. This paper shows a study case focused on a modeling system for decision-making on cognitive structure religious affiliation preference. A type-2 neurofuzzy approach is used to configure cognitive rules into an agent to build a multi-agent model for social simulation.

Influence of the Betweenness Centrality to Characterize the Behavior of Communication in a Group

The behavior of the distribution of a rumor must emerge according to the relations between the individuals. Taking as a reference that human society creates links of friendship through random encounters and conscious decisions, therefore, a rumor can be spread considering the degree of grouping that individuals have, also their location in the network and if they decide to cooperate or not. Considering the analysis of the topology that interconnects a set of individuals, relationships are detected between them that allow recognizing their centrality of degree, betweenness, and closeness. For a rumor to spread it requires that the individual has an incentive by which he decides to cooperate or not in the distribution of it. In this chapter, we propose an agent-based model that allows the identification of the central measures of each of the individuals that integrate a group which has a topology based on the Barbell’s graph.

A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo

In machine learning, hybrid systems are methods that combine different computational techniques in modeling. NetLogo is a favorite tool used by scientists with limited ability as programmers who aim to leverage computer modeling via agent-oriented approaches. This paper introduces a novel modeling framework, JT2FIS NetLogo, a toolkit for integrating interval Type-2 fuzzy inference systems in agent-based models and simulations. An extension to NetLogo, it includes a set of tools oriented to data mining, configuration, and implementation of fuzzy inference systems that modeler used within an agent-based simulation. We discuss the advantages and disadvantages of integrating intelligent systems in agent-based simulations by leveraging the toolkit, and present potential areas of opportunity.

Tracing Structures in Networks to Capture its Organization

This article analyzes the central nervous system as a dynamic complex system, focusing on the micro-interactions that allow neurons to become part of a network. The purpose of this analysis is to identify organizational levels generated in neural networks when a fault occurs and how topological structures are created during the information transmission, as in computer networks. The calculation of clustering coefficient was using as a method to observe the association of each node of the network. A dendrogram was created to reflect the changes in the interactions of the links of nodes.

Model of Interaction Among Embedded Fuzzy Agents in Ubiquitous Computing Environments

This research is motivated by the need to propose a model for studying the people interaction in educational spaces such as interactive museums, assisted by ubiquitous computing environments and aided of fuzzy logic. People are represented as mobile agent software, which has certain characteristics, limited knowledge and the necessity to interact with others to achieve individual or collective goals in a specific or dynamic context. Fuzzy agents have the capacity to deal with the uncertainty and imprecision presents by user’s interaction. We propose a model of interaction context-dependent among embedded fuzzy agents in ubiquitous computing environments that facilitate the interaction between people and the interactive museum, taking into account its ontology, autonomy, proactivity, mobility and social skills.

A complex social system simulation using type-2 fuzzy logic and multiagent system

The need of better representation of complex systems, such social systems, has made that the use of new simulation techniques are increasingly accepted, one of these accepted techniques are multi-agent systems. In addition to represent the uncertainty that is required by them, fuzzy logic and particularly type-2 fuzzy logic are being accepted. A system with three different types of agents is presented as case of study, each agent is assigned to a role with specific goals to be achieved in both ways individually and as teams, the success or failure is determined by group performance rather than individual achievement. It is also taken into account the environment or context as another type of agent. Fuzzy inference systems are defined for each of the agents to represent the concepts interpretation.