Development of the General Structure of the Knowledge Base for Neuro-Fuzzy Models

Kopyrin, Andrey; Vidishcheva, Evgeniya V.; Makarova, I. L.

doi:10.1007/978-3-030-37919-3_82

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Recording of the generated series into the universal knowledge base of the initial time series (records are formed in the following frames: FrObject, FrProperty, FrDescription, for more details about the structure of the knowledge base, see [18]).…”

Section: Resultsmentioning

confidence: 99%

“…All obtained variables are written into the knowledge base developed by the authors with a structure (object, property, relations) [18], forming a data set with set primary relationships. With further implementing the algorithm, a confidence coefficient in the interval [0, 1] is requested from the user, and for subsequent transformation, an integrated fuzzy time series is also extracted from the knowledge base, the membership function of the values of which is higher than the specified coefficient.…”

Section: Data Integrationmentioning

confidence: 99%

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Usage of the machine learning to organize time series and find anomalies

2020

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The subject of the study is the process of collecting, preparing, and searching for anomalies on data from heterogeneous sources. Economic information is naturally heterogeneous and semi-structured or unstructured. This makes pre-processing of input dynamic data an important prerequisite for the detection of significant patterns and knowledge in the subject area, so the topic of research is relevant. Pre-processing of data is several unique problems that have led to the emergence of various algorithms and heuristic methods for solving such pre-processing problems as merging and cleaning and identifying variables. In this work, an algorithm for preprocessing and searching for anomalies using LSTM is formulated, which allows you to consolidate into a single database and structure information by time series from different sources, as well as search for anomalies in an automated mode. A key modification of the preprocessing method proposed by the authors is the technology of automated data integration. The technology proposed by the authors involves the joint use of methods for building a fuzzy time series and machine lexical matching on a thesaurus network, as well as the use of a universal database built using the MIVAR concept. The preprocessing algorithm forms a single data model with the possibility of transforming the periodicity and semantics of the data set and integrating into a single information bank data that can come from various sources.

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Section: Resultsmentioning

confidence: 99%

Section: Data Integrationmentioning

confidence: 99%

Usage of the machine learning to organize time series and find anomalies

2020

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Algorithm for preprocessing and unification of time series based on machine learning for data structuring

Kopyrin¹,

Makarova²

2020

Программные Системы И Вычислительные Методы

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The subject of the research is the process of collecting and preliminary preparation of data from heterogeneous sources. Economic information is heterogeneous and semi-structured or unstructured in nature. Due to the heterogeneity of the primary documents, as well as the human factor, the initial statistical data may contain a large amount of noise, as well as records, the automatic processing of which may be very difficult. This makes preprocessing dynamic input data an important precondition for discovering meaningful patterns and domain knowledge, and making the research topic relevant.Data preprocessing is a series of unique tasks that have led to the emergence of various algorithms and heuristic methods for solving preprocessing tasks such as merge and cleanup, identification of variablesIn this work, a preprocessing algorithm is formulated that allows you to bring together into a single database and structure information on time series from different sources. The key modification of the preprocessing method proposed by the authors is the technology of automated data integration.The technology proposed by the authors involves the combined use of methods for constructing a fuzzy time series and machine lexical comparison on the thesaurus network, as well as the use of a universal database built using the MIVAR concept.The preprocessing algorithm forms a single data model with the ability to transform the periodicity and semantics of the data set and integrate data that can come from various sources into a single information bank.

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Building a standard system of inference rules based on the knowledge base

Kopyrin¹,

Kopyrina²

2021

Программные Системы И Вычислительные Методы

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The authors propose to align logical inference with the apparatus of fuzzy sets. When each solution is associated with a set of possible results with the known transitional probabilities, the solution is based on the digital information under uncertainty. Therefore, the main purpose of using fuzzy logic in expert systems consists in creation of computing devices (or software applications) that can imitate human-level reasoning and explain the techniques of decision-making. The goal of this research consists in detailed description of the reproducible standard method of setting rules of inference of the expert system for various economic subject fields, using a universal pattern of knowledgebase. For decision-making in a fuzzy system, the author suggests using the process of identification rule framework – determination of structural characteristics of fuzzy system, such as the number of fuzzy rules, number of linguistic terms the incoming variables are divided to. Such identification is conducted based on the fuzzy cluster analysis, using fuzzy decision trees. The authors present the structural chart of inference method on the basis of fuzzy logic. The presented in the article method of setting rules and fuzzy inference algorithm presented can be implemented in different areas of economics. The novelty of this work consists in automation and integration of the system for determination of fuzzy inference rules with the stage of input data collection in the subject field.

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Development of the General Structure of the Knowledge Base for Neuro-Fuzzy Models

Cited by 3 publications

References 4 publications

Usage of the machine learning to organize time series and find anomalies

Usage of the machine learning to organize time series and find anomalies

Algorithm for preprocessing and unification of time series based on machine learning for data structuring

Building a standard system of inference rules based on the knowledge base

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