scholarly journals Algorithm of classification based on fuzzy logic with expanding number of outputs

2018 ◽  
Author(s):  
D. I. Egoshkin ◽  
N. A. Guk ◽  
S. F. Siryk
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
High Speed ◽  
Automatic Generation ◽  
Knowledge Bases ◽  
Training Dataset ◽  
Production Rules ◽  
Data Types ◽  
Mathematical Methods ◽  
Actual Problem

In this article the problem of automatic generation of a knowledge base which consists of production rules for training dataset using fuzzy logic methods and a rule for comparing the values of an output variable is considered. An algorithm for the formation of fuzzy production rules is proposed. An actual problem of development and improvement of artificial intelligence algorithms and fuzzy logic application for solving a wider range of problems is considered. With the help of such systems are possible to eliminate the difficulties of formalizing knowledge about technological processes; also it is possible to organize recognition of nonstandard and emergency situations without using precise mathematical models and classical decision theory based on the tool of mathematical equations. The development of this area is relevant, as the number of tasks are constantly increasing, and the amount of knowledge becomes too large to handle them manually. The construction of an exact mathematical model for poorly formalized objects and processes are very difficult task, due to the lack of complete information. The situation becomes even more complicated if the properties of the object or process change dynamically. Therefore, the development of mathematical methods and algorithms that allow structuring the system of rules and determining the order of their calls to control consistency and completeness to optimize the number of rules, are an actual task. Modern approaches to the automation of these processes are considered. These approaches significantly improve the work of expert systems, but they allow to work only with static knowledge bases, limit the number of logical inferences and are not applicable for cases when it is necessary to add new logical rules to the existing system. In this article, an approach is developed that makes it possible to expand the knowledge base of the expert system with new rules in the process of exploitation. The developed algorithm has following advantages: high speed of problem solving; the ability that allows expanding the number of system responses without changing the scope of the rules and the program itself; expanding the range of application of fuzzy logic algorithms. The developed algorithm has following disadvantages: if the system's response database has objects that are similar to each other, they can have the same center of gravity, which in turn leads to additional checks; the minimum distance for mapping the object should be selected experimentally. The application of this algorithm can be seen on the website of the program, which classifies, maps an arbitrary user in a set of comic book characters database "CMD - Combat Marvel DC" [8]. The approach that was proposed has been successfully implemented using the C/C ++ and JavaScript languages, and JSON open-standard file format that uses human-readable text to transmit data objects consisting of attribute–value pairs and array data types. Software that was used for development: NetBeans IDE, MinGW, GNU Compiler Collection, WhiteStarUML, GitHub, WebGL, Chrome, Mozilla Firefox, Opera

scholarly journals Ontological approach to automatic generation of questions in intelligent learning systems

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (5) ◽  
pp. 44-52
Author(s):  
Li Wenzu
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Structural Characteristics ◽  
Automatic Generation ◽  
Knowledge Bases ◽  
Subject Area ◽  
Learning Systems ◽  
Multiple Choice Questions ◽  
Technical Standards ◽  
Ontological Approach

This article proposes an approach for designing a general subsystem of automatic generation of questions in intelligent learning systems. The designed subsystem allows various types of questions to be automatically generated based on information from the knowledge bases and save the generated questions in the subsystem knowledge base for future use. The main part of the subsystem is the automatic generation module of questions, which allows one to generate questions of various types based on existing question generation strategies in combination with the structural characteristics of knowledge bases built using OSTIS technology. In this article, a variety of strategies for automatically generated questions are proposed, the use of which allows various types of questions to be automatically generated, such as multiple-choice questions, fill-in-the-blank questions, questions of definition interpretation and etc. The most important part of the subsystem is the knowledge base, which stores the ontology of questions, including the question instances themselves. In this article, the knowledge base is constructed based on OSTIS technical standards. The type classification of automatically generated questions was developed, as well as the subject area for storing generated questions and the corresponding ontology described in the knowledge base of the subsystem. The generated questions are stored in the subsystem knowledge base in the form of SC-code, which is the OSTIS technology standard. When testing users, these automatically generated questions are converted to the corresponding natural language form through the natural language interface. Compared with the existing approaches, the approach proposed in this article has certain advantages, and the subsystem designed using this approach can be used in various OSTISbased systems driven by OSTIS technology.

CONSTRUCTING MODULAR HYBRID RULE BASES FOR EXPERT SYSTEMS

2001 ◽  
Vol 10 (01n02) ◽  
pp. 87-105 ◽  
Author(s):  
I. HATZILYGEROUDIS ◽  
J. PRENTZAS
Keyword(s):  
Expert Systems ◽  
Knowledge Base ◽  
Empirical Data ◽  
Knowledge Bases ◽  
Initial Training ◽  
Production Rules ◽  
Training Set ◽  
Natural Knowledge ◽  
Training Examples ◽  
Rule Bases

Neurules are a kind of hybrid rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Thus, the corresponding neurule base consists of a number of autonomous adaline units (neurules). Due to this fact, a modular and natural knowledge base is constructed, in contrast to existing connectionist knowledge bases. In this paper, we present a method for generating neurules from empirical data. To overcome the difficulty of the adaline unit to classify non-separable training examples, the notion of 'closeness' between training examples is introduced. In case of a training failure, two subsets of 'close' examples are produced from the initial training set and a copy of the neurule for each subset is trained. Failure of training any copy, leads to production of further subsets as far as success is achieved.

Design of Self-Learning Hierarchical Fuzzy Logic for Guidance and Control of Multirobot Systems

1999 ◽  
Vol 3 (6) ◽  
pp. 446-450
Author(s):  
Masoud Mohammadian ◽  
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Knowledge Base ◽  
Methodological Approach ◽  
Dynamic Environment ◽  
Knowledge Bases ◽  
Fuzzy Logic Systems ◽  
Logic Systems ◽  
Self Learning ◽  
Fuzzy Knowledge

Increased application of fuzzy logic to complex control raises a need for a structured methodological approach to developing fuzzy logic systems, which are currently developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. We propose designing self-learning hierarchical fuzzy logic control based on the integration of evolutionary algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control and collision avoidance among multiple robots. Robots are considered point masses moving in common work space. Evolutionary algorithms are used as an adaptive method for learning the fuzzy knowledge base of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems.

Open Knowledge Base: Resources and Units of Knowledge

2020 ◽  
Author(s):  
Matheus Pereira Lobo
Keyword(s):  
Knowledge Base ◽  
Knowledge Bases

This paper is about highlighting two categories of knowledge bases, one built as a repository of links, and other based on units of knowledge.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

STAND-UP MOTION SUPPORT BY A MOBILE MANIPULATOR SYSTEM WITH HIGH SPEED TACTILE SENSORS

2011 ◽  
Vol 08 (03) ◽  
pp. 181-195
Author(s):  
ZHAOXIAN XIE ◽  
HISASHI YAMAGUCHI ◽  
MASAHITO TSUKANO ◽  
AIGUO MING ◽  
MAKOTO SHIMOJO
Keyword(s):  
Fuzzy Logic ◽  
Feedback Control ◽  
Experimental Verification ◽  
High Speed ◽  
Center Of Pressure ◽  
Tactile Sensor ◽  
Mobile Manipulator ◽  
Tactile Sensing ◽  
Robot Arm ◽  
Tactile Sensors

As one of the home services by a mobile manipulator system, we are aiming at the realization of the stand-up motion support for elderly people. This work is charaterized by the use of real-time feedback control based on the information from high speed tactile sensors for detecting the contact force as well as its center of pressure between the assisted human and the robot arm. First, this paper introduces the design of the tactile sensor as well as initial experimental results to show the feasibility of the proposed system. Moreover, several fundamental tactile sensing-based motion controllers necessary for the stand-up motion support and their experimental verification are presented. Finally, an assist trajectory generation method for the stand-up motion support by integrating fuzzy logic with tactile sensing is proposed and demonstrated experimentally.

Two Approaches to the Induction of Graph-Rewriting Rules for Function-Based Design Synthesis

2016 ◽  
Author(s):  
Julian R. Eichhoff ◽  
Felix Baumann ◽  
Dieter Roller
Keyword(s):  
Engineering Design ◽  
Conceptual Design ◽  
Automatic Generation ◽  
Production Rules ◽  
Design Synthesis ◽  
Graph Rewriting ◽  
Conceptual Engineering ◽  
Frequent Subgraph ◽  
Rule Set ◽  
Rewriting Rules

In this paper we demonstrate and compare two complementary approaches to the automatic generation of production rules from a set of given graphs representing sample designs. The first approach generates a complete rule set from scratch by means of frequent subgraph discovery. Whereas the second approach is intended to learn additional rules that fit an existing, yet incomplete, rule set using genetic programming. Both approaches have been developed and tested in the context of an application for automated conceptual engineering design, more specifically functional decomposition. They can be considered feasible, complementary approaches to the automatic inference of graph rewriting rules for conceptual design applications.

scholarly journals Implementasi Fuzzy Tsukamoto Dalam Menentukan Upah Gaji Karyawan Perbulan

2020 ◽  
Vol 1 (2) ◽  
pp. 105-110
Author(s):  
Siti Maysaroh Saragih ◽  
Ayu Lestari ◽  
Mahadir Soleh Hutasuhut
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
Fuzzy Inference ◽  
A Company ◽  
Central Statistics

In a company, salary is a salary for employees who have been working for a month. However, in order to provide fair salary to all employees, the company must determine the criteria for providing salary. By using fuzzy logic, wages can be determined by going through the following stages: Fuzzification, Formation of the knowledge base, Fuzzy Inference, and Defuzzification. One of the fuzzy logic methods that can be used is the Tsukamoto method, where this method has an output in the form of firm values. To determine the salary, the data is collected from the Central Statistics Agency website in accordance with the criteria to be examined. With this research, employers can use the calculations from this research to determine salary salaries for their employees quickly, well, and precisely. So that the problem of determining the wages of their employees' salaries can be resolved properly.

scholarly journals Building an OWL ontology with Xper3

2018 ◽  
Vol 2 ◽  
pp. e25614 ◽  
Author(s):  
Florian Pellen ◽  
Sylvain Bouquin ◽  
Isabelle Mougenot ◽  
Régine Vignes-Lebbe
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Expressive Power ◽  
Knowledge Bases ◽  
User Friendliness ◽  
Standard Format ◽  
Ontology Language ◽  
Closed World ◽  
Web Standards ◽  
Description Framework

Xper3 (Vignes Lebbe et al. 2016) is a collaborative knowledge base publishing platform that, since its launch in november 2013, has been adopted by over 2 thousand users (Pinel et al. 2017). This is mainly due to its user friendly interface and the simplicity of its data model. The data are stored in MySQL Relational DBs, but the exchange format uses the TDWG standard format SDD (Structured Descriptive DataHagedorn et al. 2005). However, each Xper3 knowledge base is a closed world that the author(s) may or may not share with the scientific community or the public via publishing content and/or identification key (Kopfstein 2016). The explicit taxonomic, geographic and phenotypic limits of a knowledge base are not always well defined in the metadata fields. Conversely terminology vocabularies, such as Phenotype and Trait Ontology PATO and the Plant Ontology PO, and software to edit them, such as Protégé and Phenoscape, are essential in the semantic web, but difficult to handle for biologist without computer skills. These ontologies constitute open worlds, and are expressed themselves by RDF triples (Resource Description Framework). Protégé offers vizualisation and reasoning capabilities for these ontologies (Gennari et al. 2003, Musen 2015). Our challenge is to combine the user friendliness of Xper3 with the expressive power of OWL (Web Ontology Language), the W3C standard for building ontologies. We therefore focused on analyzing the representation of the same taxonomic contents under Xper3 and under different models in OWL. After this critical analysis, we chose a description model that allows automatic export of SDD to OWL and can be easily enriched. We will present the results obtained and their validation on two knowledge bases, one on parasitic crustaceans (Sacculina) and the second on current ferns and fossils (Corvez and Grand 2014). The evolution of the Xper3 platform and the perspectives offered by this link with semantic web standards will be discussed.

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