A heuristic algorithm for generating the numerical terms of a linguistic variable

The paper describes the implementation of a neuro-fuzzy with additional accelerated Levenberg-Marquardt Algorithm that can be used to predict credit score of Micro, Small and Medium Enterprises (MSMEs) using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with suitable membership function is used in the sense that it can bring the performance index of the network, such as the root mean squared error (RMSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g., mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low RMSE value with given training data of neuro-fuzzy network, are further fine-tuned during the network training. Finally, the above training algorithm was validated on TS type MISO neuro-fuzzy structure for credit score prediction application of MSMEs.

This paper is a revised and extended version of a paper entitled “Find the items which certainly have (most of the) important characteristics to a sufficient degree” by the same authors, prepared for the 7th World Congress of the International Fuzzy Systems Associations (IFSA’97), Prague, June 25–29, 1997 (Proceedings published by Academia, Praha, Vol. 2, pp. 243–248).

Many flexible approaches allow users to query the relational database with vague or imprecise terms similar to natural language; most of these proposals are based on the fuzzy set theory which provides an environment for working with the uncertainty described by the linguistic terms. This paper presents an overview of the most important proposals, to highlight important models concerning fuzzy query databases we consider two classifications: the first classification deals with flexible queries in crisp relational databases and the second deals with flexible queries in fuzzy relational databases. This paper aims to help researchers understand these approaches for further research in this area.

Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the kth and k - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems.

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

An important issue to extend database management systems functionality is to allow the expression of imprecise queries to make these systems able to satisfy user needs more closely. In the last few years, some authors have dealt with the problem of relaxing the relational model in order to admit some imprecision. Two significant data mining problems have been addressed recently, namely, mining fuzzy association. rules, and set-oriented mining for association rules in relational database (SETM algorithms). At present, the problem of how to express the mining fuzzy association rules algorithm as SQL queries has not been proposed. In this paper, we attempt to develop a fuzzy version of SETM algorithms that can be expressed as SQL queries in relational databases and discuss optimization of the algorithm.

This paper concerns query answering in relational databases. We assume a universe U of attributes and a set of values associated with each attribute. A database scheme is a given collection R = {1, 2, ..., n} of subsets of U, called relation schemes. A query in R is any subset of U. We call "context" any joinable subset of R. An unambiguous context is one in which a query receives the same answer independently of the subcontext used for the computation. A formal treatment of unambiguous contexts is presented and it is shown that they provide a suitable basis for discussing the universal relation assumption, the relationship uniqueness assumption, maximal objects, and other related concepts.

This paper concerns query answering in relational databases. We assume a universe U of attributes and a set of values associated with each attribute. A database scheme is a given collection R = {1, 2, ..., n} of subsets of U, called relation schemes. A query in R is any subset of U. We call "context" any joinable subset of R. An unambiguous context is one in which a query receives the same answer independently of the subcontext used for the computation. A formal treatment of unambiguous contexts is presented and it is shown that they provide a suitable basis for discussing the universal relation assumption, the relationship uniqueness assumption, maximal objects, and other related concepts.

Large-scale data analysis relies on custom code both for preparing the data for analysis as well as for the core analysis algorithms. The map-reduce framework offers a simple model to parallelize custom code, but it does not integrate well with relational databases. Likewise, the literature on optimizing queries in relational databases has largely ignored user-defined functions (UDFs). In this paper, we discuss annotations for user-defined functions that facilitate optimizations that both consider relational operators and UDFs. In this paper we focus on optimizations that enable the parallel execution of relational operators and UDFs for a number of typical patterns. A study on real-world data investigates the opportunities for parallelization of complex data flows containing both relational operators and UDFs.

The article outlines the methods for creating SQL queries in relational databases. The use of the structured query language SQL in relational databases is substantiated. It provides information about the SQL standard and the three-tier database organization system. The author describes the choice of a data model based on the conceptual level using to that end an example of the Kokand Pedagogical Institute as the relational database model. A relational conceptual diagram of the information model of a pedagogical institute is compiled. Such a conceptual diagram is depicted using a cluster. Objects of the subject area are depicted in the form of tables, which differ from each other in geometric shapes or colors. The relationships between tables in Microsoft Access are presented. The basic rules for creating and filling tables in SQL using the instructions CREATE TABLE and INSERT INTO are considered. The syntax of the SELECT statement is given. All offers of the SELECT statement and their order are listed. Examples are given for compiling simple queries and subqueries in SQL using the SELECT statement for the database of the Kokand Pedagogical Institute. Information about the order of execution of internal and external requests is given. The article considers the ORDER BY offer of a SELECT statement for sorting query results.

Induction of fuzzy rules and membership functions from training examples

In the problem of designing a Fuzzy Logic Controller (FLC) for robots in general and a Differential Drive Robot (DDR) in particular, the determination of the parameters of Membership Functions (MFs) and Fuzzy Rules (FRs) is a difficult, complicated, and time-consuming problem because this is mainly based on heuristics and the knowledge of experts. Therefore, this paper provides a new method to efficiently design an FLC for the DDR by using the Genetic Algorithm (GA). Here, the GA is used to generate and optimize both the parameters of MFs and the FRs according to the minimum kinetic energy loss criterion. For this purpose, a program is created in Google Colab® by using the Python language with the help of the “Pymoo” library to not only automatically generate all the suboptimal parameters of MFs and the suboptimal FRs but also the simulate and evaluate different used FLCs. This program is published as an open-source code so that all readers can browse, view, run, and modify the code themselves to design their FLC. The simulation results have shown that the designed FLC is much better than other used FLCs in terms of the minimum kinetic energy loss while other control performances are still good.

Schema mappings establish a correspondence between data stored in two databases, called source and target respectively. Query processing under schema mappings has been investigated extensively in the two cases where each target atom is mapped to a query over the source (called GAV, global-as-view), and where each source atom is mapped to a query over the target (called LAV, local-as-view). The general case, called GLAV, in which queries over the source are mapped to queries over the target, has attracted a lot of attention recently, especially for data exchange. However, query processing for GLAV mappings has been considered only for the basic service of query answering, and mainly in the context of conjunctive queries (CQs) in relational databases. In this paper we study query processing for GLAV mappings in a wider sense, considering not only query answering, but also query rewriting, perfectness (the property of a rewriting to compute exactly the certain answers), and query containment relative to a mapping. We deal both with the relational case, and with graph databases, where the basic querying mechanism is that of regular path queries. Query answering in GLAV can be smoothly reduced to a combination of the LAV and GAV cases, and for CQs this reduction can be exploited also for the remaining query processing tasks. In contrast, as we show, GLAV query processing for graph databases is non-trivial and requires new insights and techniques. We obtain upper bounds for answering, rewriting, and perfectness, and show decidability of relative containment.

In this paper, we develop techniques to produce interoperable queries with object and relational databases. A user poses a local query in a local query language, against a local object or relational schema. We transparently produce appropriate queries with respect to a remote target object or relational schema, corresponding to some remote database which contains data relevant to the user's query. Mapping knowledge to resolve representational heterogeneities in local and remote schemas is expressed in a canonical representation, CRmapping, and is independent of the particular data model. A canonical representation CRquery is also used to resolve heterogeneities of query languages. A set of heterogeneous transformation algorithms define the appropriate transformations from the local queries to the remote queries. The use of canonical representations (CR) allows us to represent queries independent of the particular query language, and to resolve representational conflicts in a uniform manner, independent of models and query languages.

Objectives Development of a method for selecting the type of accessory function and obtaining its parameters to allow subjective personal influences in automated information processing to be excluded.Method. Existing methods for constructing membership functions were analysed. The research was based on the methods of fuzzy logic and data analysis.Results. A method for obtaining the parameters of membership functions of fuzzy sets using real data is suggested. It is proposed to use the data obtained from the object under study to determine the kernel of the fuzzy number, as well as derive theoretical information about the object for the carrier. Triangular, trapezoidal, bell-shaped and Gaussian membership functions are considered. The appearance of the membership function can be defined using the criterion of the relations of the kernel to the carrier of a fuzzy set. The results of calculations for obtaining the membership functions based on data on the power consumption of electric motors of different types are given.Conclusion. The developed method can be used both in decision support systems as well as in automated systems for controlling technological processes. If necessary, the values of the criterion proposed in the article can be revised to take the values included in the set of measured real data into account or to simplify the procedure of automated processing. Further research will use the described method to obtain the terms of linguistic variables.