Synthesis of a Fuzzy Logic Model Rule Base through Supervised Learning

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 112-120
Author(s):  
Viktor N. Novikov ◽  
Keyword(s):  
Supervised Learning ◽  
Expert Knowledge ◽  
Binary Classification ◽  
Fuzzy Model ◽  
Supervised Machine Learning ◽  
Coordinate Space ◽  
Rule Base ◽  
Membership Functions ◽  
Fuzzy Classifier ◽  
Model Rule

Intelligent information systems are at present actively incorporated in almost all industry fields. Out of many approaches to construction of such systems, the following two methods are worthy of noting: a method involving the use of expert knowledge, which include, in particular, fuzzy modeling), and a method of supervised machine learning, which estimate this knowledge from the available data marked depending on the objectresponse pairs. Each of these methods has its essential advantages and drawbacks. A combined use of both the approaches for solving the classification case is given. The proposed method for training a fuzzy classifier includes fuzzification of the input features of objects, shaping of logical rule conditions for the fuzzy model rule base, and selection of the most typical rule conclusions for filling the relational matrix. The terms of input features and their membership functions behave as the fuzzy model hyperparameters. As an example, the case of binary classification in a two-dimensional coordinate space with a nonlinear distribution of classes is considered. The sets of terms and membership functions ensuring high-quality classification on the training and test data have been found for each of the coordinates. The fuzzy classifier trained using the proposed method is able to solve a number of binary and multi-class classification cases. It can be applied in a situation involving difficulties in specifying an a priori rule base, and supervised learning is allowed to be used as a solution. Since the approach has logical rules at its heart, transparency of the model is ensured, and explanations to the results yielded by the model can be done.

Implementing Fuzzy Learning Algorithms in a 6 DOF Hydraulic Parallel Link Manipulator: Actuators' Fuzzy Modeling

2002 ◽  
Vol 14 (4) ◽  
pp. 408-419 ◽  
Author(s):  
Zakarya Zyada ◽  
◽  
Yasuhisa Hasegawa ◽  
Gancho Vachkov ◽  
Toshio Fukuda
Keyword(s):  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Hydraulic Actuator ◽  
Fuzzy Rule Base ◽  
Learning Rules ◽  
Model Rule ◽  
Parallel Link ◽  
Fuzzy Learning ◽  
Good Agreement

A fuzzy-logic-based model, suitable for force control, for each hydraulic actuator of a parallel link manipulator is presented. Constructing the fuzzy model rule base mainly consists of 2 stages: (1) learning rules from examples for the known acquired input/output data of the hydraulic actuators and (2) completing unknown fuzzy rules from heuristics and experience based on the logic of actuators' behavior. We first present the algorithm of fuzzy-rule base modeling and its application for one actuator. We then present fuzzy rule base results characterizing each hydraulic actuator, differing from one to another, of a 6 DOF parallel link manipulator. Simulation output results from fuzzy models show good agreement with experimental results.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals ANALYSIS OF THE EFFECTIVENESS OF TECHNOLOGICAL INNOVATIONS BY FUZZY MODELING METHODS: THE CONTENT OF THE MODEL AND THE CONDITIONS FOR SOFTWARE IMPLEMENTATION

2019 ◽  
Vol 29 (5) ◽  
pp. 609-620
Author(s):  
O.M. Shatalova
Keyword(s):  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Technological Development ◽  
Innovation Management ◽  
Fuzzy Model ◽  
Software Implementation ◽  
Technological Innovations ◽  
Fuzzy Evaluation ◽  
Innovative Strategy ◽  
Intelligent Methods

The article is devoted to the development of methods and instruments for solving the problem of non-stochastic uncertainty in the management of technological innovations. In this regard, the methodology of fuzzy-multiple modeling of systems is considered. It allows you to take into account both deterministic and stochastic data, as well as mental knowledge of the system on the part of decision makers, presented in the lexical description and based on fuzzy evaluation mechanism. The construction of fuzzy-multiple models is aimed at reproducing the logic of decision making and is based on the use of intelligent methods of information processing, including those presented in fuzzy and verbal characteristics, by mathematical language means, which can be transferred to machine processing. The basis of the study is the provision on the vector form of the efficiency indicator and the implementation of the correspondence function between the basic parameters of efficiency through fuzzy inference. The article describes the developed basic conditions for simulation of fuzzy-multiple modeling in assessing the effectiveness of technological innovation management systems - the structure of the model and methods for its construction; presents the means of software implementation of a simulation fuzzy-plural model developed in accordance with these conditions and the results of its practical testing. The developed conditions of fuzzy-multiple modeling in assessing the effectiveness of technological innovations form the basis of a comprehensive analysis of the conditions of technological development of the enterprise, allow to identify significant management factors and form the content of an effective innovative strategy for the technological development of the enterprise; the fuzzy model itself can be considered as a platform for the integration of deterministic, stochastic, expert knowledge of the system.

scholarly journals A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Gestational Diabetes Prediction

2021 ◽  
Vol 9 (12) ◽  
pp. 1001-1005
Author(s):  
Lokesh Kola
Keyword(s):  
Machine Learning ◽  
Gestational Diabetes ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
World Health ◽  
Middle Income ◽  
Diabetes Prediction ◽  
Supervised Machine Learning Classifiers ◽  
Health Organization

Abstract: Diabetes is the deadliest chronic diseases in the world. According to World Health Organization (WHO) around 422 million people are currently suffering from diabetes, particularly in low and middle-income countries. Also, the number of deaths due to diabetes is close to 1.6 million. Recent research has proven that the occurrence of diabetes is likely to be seen in people aged between 18 and this has risen from 4.7 to 8.5% from 1980 to 2014. Early diagnosis is necessary so that the disease does not go into advanced stages which is quite difficult to cure. Significant research has been performed in diabetes predictions. As time passes, challenges keep increasing to build a system to detect diabetes systematically. The hype for Machine Learning is increasing day to day to analyse medical data to diagnose a disease. Previous research has focused on just identifying the diabetes without specifying its type. In this paper, we have we have predicted gestational diabetes (Type-3) by comparing various supervised and semi-supervised machine learning algorithms on two datasets i.e., binned and non-binned datasets and compared the performance based on evaluation metrics. Keywords: Gestational diabetes, Machine Learning, Supervised Learning, Semi-Supervised Learning, Diabetes Prediction

scholarly journals Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Hamed Kharrati ◽  
Sohrab Khanmohammadi ◽  
Witold Pedrycz ◽  
Ghasem Alizadeh
Keyword(s):  
Stability Analysis ◽  
Nonlinear Dynamical Systems ◽  
Fitness Function ◽  
Fuzzy Model ◽  
Taylor Series Expansion ◽  
Fuzzy Modeling ◽  
Lyapunov Theory ◽  
Membership Functions ◽  
Polynomial Fuzzy Systems ◽  
Nonlinear Plant

This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB) systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA) is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS) approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS-based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method.

Predicting Convective Storm Characteristics using Machine Learning from Hi-Resolution NWP Forecasts

2021 ◽  
Author(s):  
Aniel Jardines
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Binary Classification ◽  
Air Traffic ◽  
Mitigation Strategies ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Traffic Flow Management ◽  
Storm Characteristics ◽  
Convective Weather

<p>Convective weather represents a significant disruption to air traffic flow management (ATFM) operations. Thunderstorms are the cause for a substantial amount of delay in both the en-route and airport environment. Before the day of operations, poor prediction capability of convective weather prohibits traffic managers from considering weather mitigation strategies during the pre-tactical phase of ATFM planning. As a result, convective weather is mitigated tactically, possibly leading to excessive delays.  </p><p>The skill of weather forecasting has greatly improved in recent years. Hi-resolution weather models can predict the future state of the atmosphere for some weather parameters. However, incorporating the output from these sophisticated weather products into an ATFM solution that provides easily interpreted information by the air traffic managers remains a challenge. </p><p>This paper combines data from high-resolution numerical weather predictions with actual storm observations from lightning detecting and satellite images. It applies supervised machine learning techniques such as binary classification, multiclass classification, and regression to train neural networks to predict the occurrence, severity, and altitude of thunderstorms. The model predictions are given up to 36hr in advance, within timeframes necessary for pre-tactical planning of ATFM, providing traffic managers with valuable information for developing weather mitigation plans. </p>

Semantics-Based Document Categorization Employing Semi-Supervised Learning

2015 ◽  
pp. 112-140 ◽  
Author(s):  
Jan Žižka ◽  
František Dařena
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Real World ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Learning Method ◽  
Label Information ◽  
Document Categorization

The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such data originating from the Internet. Having a sufficient number of labeled examples, a suitable supervised machine learning-based classifier can be trained. When no labeling is available, an unsupervised learning method can be applied, however, the missing label information often leads to worse classification results. This chapter demonstrates a method based on semi-supervised learning when a smallish set of manually labeled examples improves the categorization process in comparison with clustering, and the results are comparable with the supervised learning output. For the illustration, a real-world dataset coming from the Internet is used as the input of the supervised, unsupervised, and semi-supervised learning. The results are shown for different number of the starting labeled samples used as “seeds” to automatically label the remaining volume of unlabeled items.

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