Coevolutionary fuzzy modeling

This thesis presents Fuzzy CoCo, a novel approach for system design, conducive to explaining human decisions. Based on fuzzy logic and coevolutionary computation, Fuzzy CoCo is a methodology for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (precision) and linguistic representation (interpretability). From a numeric point of view, fuzzy systems exhibit nonlinear behavior and can handle imprecise and incomplete information. Linguistically, they represent knowledge in the form of rules, a natural way for explaining decision processes. Fuzzy modeling —meaning the construction of fuzzy systems— is an arduous task, demanding the identification of many parameters. This thesis analyses the fuzzy-modeling problem and different approaches to coping with it, focusing on evolutionary fuzzy modeling —the design of fuzzy inference systems using evolutionary algorithms— which constitutes the methodological base of my approach. In order to promote this analysis the parameters of a fuzzy system are classified into four categories: logic, structural, connective, and operational. The central contribution of this work is the use of an advanced evolutionary technique —cooperative coevolution— for dealing with the simultaneous design of connective and operational parameters. Cooperative coevolutionary fuzzy modeling succeeds in overcoming several limitations exhibited by other standard evolutionary approaches: stagnation, convergence to local optima, and computational costliness. Designing interpretable systems is a prime goal of my approach, which I study thoroughly herein. Based on a set of semantic and syntactic criteria, regarding the definition of linguistic concepts and their causal connections, I propose a number of strategies for producing more interpretable fuzzy systems. These strategies are implemented in Fuzzy CoCo, resulting in a modeling methodology providing high numeric precision, while incurring as little a loss of interpretability as possible. After testing Fuzzy CoCo on a benchmark problem —Fisher's Iris data— I successfully apply the algorithm to model the decision processes involved in two breast-cancer diagnostic problems: the WBCD problem and the Catalonia mammography interpretation problem. For the WBCD problem, Fuzzy CoCo produces systems both of high performance and high interpretability, comparable (if not better) than the best systems demonstrated to date. For the Catalonia problem, an evolved high-performance system was embedded within a web-based tool —called COBRA— for aiding radiologists in mammography interpretation. Several aspects of Fuzzy CoCo are thoroughly analyzed to provide a deeper understanding of the method. These analyses show the consistency of the results. They also help derive a stepwise guide to applying Fuzzy CoCo, and a set of qualitative relationships between some of its parameters that facilitate setting up the algorithm. Finally, this work proposes and explores preliminarily two extensions to the method: Island Fuzzy CoCo and Incremental Fuzzy CoCo, which together with the original CoCo constitute a family of coevolutionary fuzzy modeling techniques. The aim of these extensions is to guide the choice of an adequate number of rules for a given problem. While Island Fuzzy CoCo performs an extended search over different problem sizes, Incremental Fuzzy CoCo bases its search power on a mechanism of incremental evolution

Sistem Pendukung Keputusan Penilaian Karyawan Terbaik PT. Suteckariya Indonesia dengan Metode Analytical Hierarchy Process

Dalam sebuah Perusahaan, karyawan adalah salah satu komponen bagian penentu keberhasilan suatu Perusahaan. Tenaga kerja yang berkualitas akan memudahkan Perusahaan dalam mengelola aktivitasnya sehingga tujuan yang ditetapkan dapat tercapai. Untuk mendapatkan tenaga kerja (Sumber Daya Manusia /SDM) yang berkualitas bukanlah hal yang mudah. Hal tersebut berkaitan pada suatu momen untuk mengambil sebuah keputusan. Kemampuan mengambil keputusan yang cepat dan cermat menjadi kunci keberhasilan dalam persaingan global dan untuk mengambil sebuah keputusan tentu diperlukan analisis-analisis dan perhitungan yang matang dan tergantung kepada banyak sedikitnya kriteria yang mempengaruhi permasalahan yang membutuhkan suatu keputusan. Pengambilan suatu keputusan dengan banyak kriteria memerlukan suatu cara penanganan khusus terutama bila kriteria pengambilan keputusan tersebut saling terkait.Untuk itu dibutuhkan suatu model sebelum keputusan diambil. Dari penjelasan diatas, maka penulis ingin membuat model pengambilan keputusan yang dapat menjadi rujukan dalam proses penilaian karyawan terbaik di PT. SURTECKARIYA INDONESIA, sehingga diharapkan bisa menseleksi karyawan yang sesuai dengan kriteria dan kebutuhan Perusahaan

Metodología para la generación automática de reglas borrosas y ajuste adaptativo de funciones de pertenencia por medio de una arquitectura de red neural netfuz 1.0

In the generation of the Fuzzy Inference Systems, the primordial task is the extraction and the tuning of the memberships functions and the fuzzy rules. However, when using the traditional methods to carry out this task, the obtained results are not the prospective ones and in most of cases serious inconveniences are presented. This article presents a methodological proposal base in Artificial Neural Networks that allows extracting the fuzzy rules and the parameters of the functions of membership of a Fuzzy Inference System type Sugeno automatically, leaving of a group of data input-output. The development of a software is contemplated that will facilitate the application in the control of processes, the prediction and the estimate of parameters.En la generación de los Sistemas de Inferencia Borrosos, la tarea primordial es la extracción y el ajuste de las funciones de pertenencia y las reglas borrosas. Sin embargo, al usar los métodos tradicionales para realizar esta tarea, los resultados obtenidos no son los esperados y en la mayoría de casos se presentan graves inconvenientes. Este artículo presenta una propuesta metodológica basada en Redes Neuronales Artificiales que permite extraer automáticamente las reglas borrosas y los parámetros de las funciones de membresía de un Sistema de Inferencia Borroso tipo Sugeno, partiendo de un conjunto de datos entrada-salida. Se contempla el desarrollo de un software que facilitará la aplicación en el control de procesos, la predicción y la estimación de parámetros

Diagnosing Hepatitis Using Hybrid Fuzzy-CBR

The Malaysia populations are currently estimated to be 28.9 million with a number of medical specialists is 2,500 and 20,280 doctors. This ratio figures to cause patients need to wait longer in government hospitals and clinics before they can meet doctor or medical specialist. In order to resolve this problem, Ministry of Health has pledged to reduce waiting time of patient examination from 45 minutes to 30 minutes by provide allocation of large budget to the medical sector. This budget will be used either to buy new equipment, which can work with large capacity or upgrade the old equipment to work faster or build the new hospital to tend more patients or hire other doctors from overseas. Due to that reason and the coming which World Hepatitis Day on 28 July 2012, this study proposes a the use of hybrid intelligent, which combine Fuzzy Logic and Case-Based Reasoning (CBR) approach that could be integrated in the diagnosis system to classify patient condition by using fuzzy technique and similarity measurement based on current symptoms of a hepatitis patient. Focus of this study is to develop an automated decision support system that can be used by the doctors to accelerate diagnosis processing. As a result, a prototype called Intelligent Medical Decision Support System (IMDSS) using Fuzzy-CBR engine for diagnosis purposes has been developed, validated and evaluated in this study. The finding through validation and evaluation phase indicates that IMDSS is reliable in assisting doctors during the diagnosis process. In fact, the diagnosis of a patient has become easier than the manual process and easy to use

Metodología para la generación automática de reglas borrosas y ajuste adaptativo de funciones de pertenencia por medio de una arquitectura de red neural netfuz 1.0

In the generation of the Fuzzy Inference Systems, the primordial task is the extraction and the tuning of the memberships functions and the fuzzy rules. However, when using the traditional methods to carry out this task, the obtained results are not the prospective ones and in most of cases serious inconveniences are presented. This article presents a methodological proposal base in Artificial Neural Networks that allows extracting the fuzzy rules and the parameters of the functions of membership of a Fuzzy Inference System type Sugeno automatically, leaving of a group of data input-output. The development of a software is contemplated that will facilitate the application in the control of processes, the prediction and the estimate of parameters.En la generación de los Sistemas de Inferencia Borrosos, la tarea primordial es la extracción y el ajuste de las funciones de pertenencia y las reglas borrosas. Sin embargo, al usar los métodos tradicionales para realizar esta tarea, los resultados obtenidos no son los esperados y en la mayoría de casos se presentan graves inconvenientes. Este artículo presenta una propuesta metodológica basada en Redes Neuronales Artificiales que permite extraer automáticamente las reglas borrosas y los parámetros de las funciones de membresía de un Sistema de Inferencia Borroso tipo Sugeno, partiendo de un conjunto de datos entrada-salida. Se contempla el desarrollo de un software que facilitará la aplicación en el control de procesos, la predicción y la estimación de parámetros

Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

Machine Learning is concerned in constructing models which can learn and make predictions based on data. Rule extraction from real world data that are usually tainted with noise, ambiguity, and uncertainty, automatically requires feature selection. Neuro-Fuzzy system (NFS) which is known with its prediction performance has the difficulty in determining the proper number of rules and the number of membership functions for each rule. An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian classifier (GA-FBC) was proposed to help the NFS in the rule extraction. Feature selection was performed in the rule level overcoming the problems of the FBC which depends on the frequency of the features leading to ignore the patterns of small classes. As dealing with a real world problem such as the Air/Fuel Ratio (AFR) prediction, a multi-objective problem is adopted. The GA-FBC uses mutual information entropy, which considers the relevance between feature attributes and class attributes. A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. Predictive accuracy and the complexity of the Fuzzy Rule Base System (FRBS) including number of rules and number of terms in each rule were taken as terms of evaluation. It was also compared to the original GA-FBC depending on the frequency not on Mutual Information (MI). Experimental results using Air/Fuel Ratio (AFR) data sets show that the new model participates in decreasing the average number of attributes in the rule and sometimes in increasing the average performance compared to other models. This work facilitates in achieving a self-generating FRBS from real data. The GA-FBC can be used as a new direction in machine learning research. This research contributes in controlling automobile emissions in helping the reduction of one of the most causes of pollution to produce greener environment

Diagnóstico del cáncer de mama empleando clasificador difuso

Los sistemas de clasificación difusa generados a partir de datos experimentales presentan una alta precisión pero a costa de sacrificar la integridad semántica del modelo difuso obtenido. En este artículo se presenta un método novedoso para generar sistemas de clasificación difusa a partir de datos, que superan el conflicto entre precisión e interpretabilidad, obteniendo modelos con particiones triangulares de solapamiento 0.5 en sus antecedentes y consecuentes tipo singleton. Para la ponderación de los antecedentes se utiliza un operador de combinación en vez de una T-norma, lo que contribuye a una reducción sustancial en el número de reglas

Diseño de circuitos analógicos basados en amplificadores operacionales usando algoritmos genéticos con función de aptitud difusa

This paper presents a genetic algorithm approach to the design of analog circuits consisting of operational amplifiers. The input of the algorithm is the transfer function of the required system. The fitness function of the genetic algorithm is implemented by means of a fuzzy inference system. A summary of the methodology used in the design is included and results with a specific circuit topology for multiple transfer functions are reported.Este artículo presenta una propuesta para el diseño de circuitos analógicos basados en amplificadores operacionales usando un algoritmo genético simple. La entrada al algoritmo es la función de transferencia requerida por el diseñador expresada como la respuesta al escalón unitario que el circuito debería exhibir. Adicionalmente, una característica especial del algoritmo radica en que la función de aptitud se implementa como un sistema de inferencia difusa. Se incluye en el artículo un resumen de la metodología utilizada para el diseño del algoritmo y resultados con múltiples funciones de transferencia para un circuito de topología específica.  

Sistema de inferencia difusa basado en relaciones Booleanas

This paper describes a fuzzy-inference system that is basedon Boolean relations. Boolean logic is recognized as a usefultool for automata and digital systems design. An alternativeto improve automata systems consists in smoothing the crispsets into fuzzy sets. The purpose to perform a continuousaction on the actuator; by making this change, a new fuzzyinference system based on Boolean relations arises.Although the original proposal was formulated so as toconsider automata systems, it is clear that this approach canbe extended to more general fuzzy inference systems.Este documento describe la estructura de un sistema de inferencia difusa basado en relaciones booleanas. La teoría relacionada con lógica y conjuntos booleanos es una buena herramienta para el diseño de automatismos y sistemas digitales. Una variación con la cual se busca mejorar los sistemas basados en automatismos consiste en emplear conjuntos difusos en lugar de booleanos. Lo anterior se realiza con el objetivo de tener una acción continua en el actuador del automatismo. Al realizar esta variación y al aplicar la metodología de diseño de los sistemas de automatismos, aparecen los sistemas de inferencia difusa basados en relaciones booleanas.Aunque inicialmente esta propuesta se realizó considerando sistemas de automatismos, se observa que es posible extenderla a sistemas de inferencia difusa

Implementación de Lógica Difusa para realizar Pruebas de Hipótesis Estadísticas Univariadas

This paper shows a fuzzy logic approach to univariate statistical hypothesis testing. This tests are an inferential tools to determine whether a statement about the value of a population’s parameter should be rejected or not. Classical statistical techniques used in hypothesis testing have an important limitation, because does not show the fulfillment level of the used sample faced with the population of interest. The fuzzy application wants to offer, to the appraiser of a sample of a population, a value that indicates the fulfillment level of the statement, in order to improve the decisions making about the sample.El siguiente artículo muestra un acercamiento de la lógica difusa a las pruebas de hipótesis estadísticas, las cuales son una herramienta inferencial para determinar si una afirmación del valor de un parámetro de una poblacióndebe ser rechazada o no. Las técnicas estadísticas clásicas para realizar pruebas de hipótesis poseen una limitante importante, ya que no se muestra algún grado de cumplimiento de la muestra utilizada frente a la población de interés. La aplicación difusa pretende brindar, a quien evalúa una muestra de una población, un valor que indique el grado de cumplimiento de la afirmación, con el fin de tomar decisiones acerca de la muestra