On the Solvability and Number of Solutions of Production-Logical Equations in a Fuzzy LP-Structure

For the construction and study of formal models of intelligent information systems, algebraic methods are useful. One of the topical directions here is the production-type logical systems, which are widespread in computer science. In recent years, the author and his followers have been developing the algebraic theory of LP-structures (lattice production structures). It is designed to formalize and solve a number of knowledge management problems in production systems. The method of relevant backward inference (LP-inference) was also introduced and investigated, which significantly reduces the number of calls to external information sources in comparison with classical inference. Subsequently, the theory was generalized to speed up inference in distributed production systems. At the same time, modern intelligent systems are characterized by fuzzy knowledge and fuzzy reasoning. Therefore, there is a need to extend the theory of LP-structures to fuzzy production systems. This research was initiated in previous articles by the author. Some concepts are introduced that impart fuzziness to LP-structures, and certain properties of fuzzy LP-inference are established. In recent works, research results are presented that systematically generalize the theory of LP-structures to the case of fuzzy knowledge bases. The terminology of FLP-structures with fuzzy logical relation (Fuzzy LP-structures) is introduced, the main standard properties are proved. The present work complements the FLP-structure model by investigating a class of production-logical equations. Relevant inference ideas are based on it, reducing the number of calls to external sources of information. Methods for solving these equations are formulated. For the first time, questions about the existence and number of solutions have been resolved. Finding a solution to a production-logical equation corresponds to the backward fuzzy inference in a production system. The proved theorems can be used for software implementation of fuzzy LP-structures and corresponding optimization of fuzzy inference. Some ideas for this implementation are discussed.

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Methods for Solving Production-Logical Equations in a Fuzzy LP-Structure

PROGRAMMNAYA INGENERIA ◽

10.17587/prin.11.342-348 ◽

2020 ◽

Vol 11 (6) ◽

pp. 342-348

Author(s):

S. D. Makhortov ◽

Keyword(s):

Intelligent Systems ◽

Fuzzy Inference ◽

Production Systems ◽

Algebraic Theory ◽

Knowledge Bases ◽

Logical Relation ◽

Sources Of Information ◽

Logical Equation ◽

Production Type ◽

Wide Range

Algebraic methods provide an effective formalism for constructing and researching models of a wide range of information systems, especially intelligent ones. This provision fully applies to the production-type logical systems widespread in computer science. In the last decade, the author has created an algebraic theory of LP-structures (lattice production structures), which makes it possible to effectively solve a number of important problems related to production systems. Such tasks include equivalent transformations, verification, minimization of knowledge bases, and acceleration of logical inference. In particular, the method of relevant backward inference (LP-inference) was introduced and investigated, which significantly reduces the number of calls to external sources of information. Subsequently, this theory was expanded to model of distributed production systems. An important property of modern intelligent systems is the fuzzy nature of knowledge and reasoning. Therefore, an urgent problem arises of extending the advantages of the theory of LP-structures to fuzzy production systems. The beginning of this direction was laid in the previous articles of the author. Concepts describing the fuzzy LP-structure were introduced, and some useful properties of fuzzy LP-inference were studied. In recent works, studies are presented that systematically generalize the theory of LP-structures for managing fuzzy knowledge bases. The basic terminology of FLP-structures with a fuzzy logical relation (Fuzzy LP-structures) is introduced, the basic properties are proved — closedness, the existence of a canonical form and logical reduction. This work complements this model by defining and investigating the apparatus of production-logical equations in the FLP-structure. Methods for solving these equations are proposed and substantiated. Finding a solution to the production-logical equation corresponds to the backward fuzzy inference. The presented theorems provide a theoretical basis for further advances in the field of optimization of fuzzy inference. As a continuation of the work, it is planned to consider questions about the direct solvability of simplified equations and the number of their solutions.

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Artificial Intelligence in General

Advances in Human and Social Aspects of Technology - Human Behavior and Another Kind in Consciousness ◽

10.4018/978-1-5225-8217-5.ch001 ◽

2019 ◽

pp. 1-16

Keyword(s):

Artificial Intelligence ◽

Computer Aided Design ◽

Intelligent Systems ◽

Production Systems ◽

Knowledge Bases ◽

Computer Integrated Manufacturing ◽

Current Status ◽

Computer Aided ◽

Control Computer ◽

History Of

There are many kinds of uses for artificial intelligence (AI) in almost every field. AI is quite often used for control, computer aided design (CAD) and computer aided manufacturing (CAM), machine control, computer integrated manufacturing (CIM), production spot control, factory control, intelligent control, intelligent systems, deep learning, the cloud, knowledge bases, database, management, production systems, statistics, to assist sales forces, environment examination, agriculture, art, livings, daily life, etc. The present AI uses will be reexamined whether there is any matter to be considered further or not in AI research directions and their purposes behind the current status by looking at the history of AI development.

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Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

PROBLEMS IN PROGRAMMING ◽

10.15407/pp2016.02-03.141 ◽

2016 ◽

pp. 141-149

Author(s):

S.V. Yershov ◽

◽

R.М. Ponomarenko ◽

Keyword(s):

Dynamic Models ◽

Fuzzy Inference ◽

Knowledge Bases ◽

Comparative Characteristic ◽

Software Systems ◽

Scientific Papers ◽

Speed Up ◽

Diagnostic Software ◽

Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

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Real/Binary-Like Coded Genetic Algorithm to Automatically Generate Fuzzy Knowledge Bases

The Fourth International Conference on Control and Automation 2003 ICCA Final Program and Book of Abstracts ICCA-03 ◽

10.1109/icca.2003.1595133 ◽

2003 ◽

Cited By ~ 1

Author(s):

S. Achiche ◽

L. Baron ◽

M. Balazinski

Keyword(s):

Genetic Algorithm ◽

Knowledge Bases ◽

Fuzzy Knowledge Bases ◽

Fuzzy Knowledge

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The use of fuzzy databases and knowledge bases for aiding engineering projects

Budownictwo i Architektura ◽

10.35784/bud-arch.2175 ◽

2013 ◽

Vol 12 (1) ◽

pp. 069-076

Author(s):

Janusz Szelka ◽

Zbigniew Wrona

Keyword(s):

Decision Making ◽

Database Systems ◽

Knowledge Bases ◽

Fuzzy Data ◽

Fuzzy Knowledge Bases ◽

Decision Making Processes ◽

Engineering Projects ◽

Fuzzy Knowledge

The IT tools that are widely used for aiding information and decision-making tasks in engineering activities include classic database systems, and in the case of problems with poorly-recognised structure – systems with knowledge bases. The uniqueness of these categories of systems allows, however, neither to represent the approximate or imprecise nature of available data or knowledge nor to process fuzzy data. Since so far there have been no solutions related to the use of fuzzy databases or fuzzy knowledge bases in engineering projects, it seems necessary to make an attempt to assess the possible employment of these technologies to aid analytical and decision-making processes.

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Fundamentals of creating MIVAR expert systems

10.12737/1513119 ◽

2021 ◽

Author(s):

Oleg Varlamov

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Expert System ◽

Expert Systems ◽

Decision Support Systems ◽

Intelligent Systems ◽

Autonomous Robots ◽

Support Systems ◽

Knowledge Bases ◽

Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Disambiguation and Filtering Methods in Using Web Knowledge for Coreference Resolution

Cross-Disciplinary Advances in Applied Natural Language Processing ◽

10.4018/978-1-61350-447-5.ch013 ◽

2012 ◽

pp. 185-201 ◽

Cited By ~ 1

Author(s):

Olga Uryupina ◽

Massimo Poesio ◽

Claudio Giuliano ◽

Kateryna Tymoshenko

Keyword(s):

Semantic Information ◽

Knowledge Bases ◽

Coreference Resolution ◽

Number Of Solutions ◽

Web Resources ◽

Percentage Points ◽

Resolution System ◽

Semantic Compatibility ◽

Additional Constraints ◽

Web Knowledge

The authors investigate two publicly available Web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution engine. They extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a coreference resolution system. The authors show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings (Ponzetto & Poesio, 2009). They propose, therefore, a number of solutions to reduce the amount of noise coming from Web resources: using disambiguation tools for Wikipedia, pruning Yago to eliminate the most generic categories and imposing additional constraints on affected mentions. The evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves the system’s performance by 2-3 percentage points.

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Prediction of International Stock Markets Based on Hybrid Intelligent Systems

Handbook of Research on Innovations in Information Retrieval, Analysis, and Management - Advances in Knowledge Acquisition, Transfer, and Management ◽

10.4018/978-1-4666-8833-9.ch004 ◽

2016 ◽

pp. 110-124

Author(s):

Salim Lahmiri

Keyword(s):

Neural Networks ◽

Time Delay ◽

Stock Market ◽

Intelligent Systems ◽

Fuzzy Inference ◽

Intelligent System ◽

Market Price ◽

Hybrid Intelligent Systems ◽

Out Of Sample ◽

Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

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Intelligent Systems for Structural Damage Assessment

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0193 ◽

2018 ◽

Vol 29 (1) ◽

pp. 378-392

Author(s):

Eleni Vrochidou ◽

Petros-Fotios Alvanitopoulos ◽

Ioannis Andreadis ◽

Anaxagoras Elenas

Keyword(s):

Damage Assessment ◽

Intelligent Systems ◽

Structural Damage ◽

Fuzzy Inference ◽

Damage Index ◽

Reinforced Concrete Structure ◽

Ann Model ◽

Inference System ◽

Time Frequency ◽

Artificial Neural Network Ann

Abstract This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.

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