Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization

This paper proposes the universal information technology for designing the rule bases (RB) with the formation of optimal consequents for fuzzy systems (FS) of different types on the basis of ant colony optimization (ACO) techniques. The developed ACO-based information technology allows effectively synthesizing rule bases of various dimensions both for the MISO and MIMO fuzzy systems taking into account the particular features of the RB consequents formation in the conditions of insufficient initial information. In order to study and validate the efficiency of the presented information technology the design of the RB for the adaptive fuzzy control system of the ship steering device is carried out in this work. The computer simulations results show that adaptive control system with developed RB provides achievement of high enough quality indicators of rudder angle control. Thus, application of the proposed ACO-based information technology allows designing effective RB with optimal consequents by means of minor computational costs that, in turn, confirms its high efficiency.

Z-Hesitant Fuzzy Network Model with Reliability and Transparency of Information for Decision Systems

Decision-making environment often encounters complexity along its processes, especially in the context of multidisciplinary scientific research. This can commonly be seen in engineering, computing, finance, astrology and other different areas. It is of great restriction in dealing with the practical problems which have diverse demands and properties. There is a growing body of literature that recognizes the importance of dealing with the complexity in decision making environment. The reliability and the transparency are the dominant feature of the integration of fuzzy network and Z-numbers. However, much of the research up to now has been descriptive in nature of the features. Hence, this proposed method is unique and novel because it offers some interesting insight of dealing with reliability and transparency of information in Z-hesitant fuzzy network decision-making environment. The fuzzy networks have the functionality under rule bases of fuzzy systems where it is recognized by its transparency and precision. The proposed method makes use of fuzzy network with the incorporation of hesitant fuzzy sets to assimilate decision information towards alternatives. For the validation and applicability purposes of the proposed method, the case study of stock evaluation assessed by a number of decision makers has been utilized as a real-world problem. The performance of the proposed method is evaluated respectively by applying the Spearman’s rho correlation. The result shows that the proposed method performs as the established method with the consideration of additional dominant features.

Methods for Developing Models in a Fuzzy Environment of Reactor and Hydrotreating Furnace of a Catalytic Reforming Unit

Methods for the development of fuzzy and linguistic models of technological objects, which are characterized by the fuzzy output parameters and linguistic values of the input and output parameters of the object are proposed. The hydrotreating unit of the catalytic reforming unit was investigated and described. On the basis of experimental and statistical data and fuzzy information from experts and using the proposed methods, mathematical models of a hydrotreating reactor and a hydrotreating furnace were developed. To determine the volume of production from the outlet of the reactor and furnace, nonlinear regression models were built, and fuzzy models were developed in the form of fuzzy regression equations to determine the quality indicators of the hydrotreating unit—the hydrogenated product. To identify the structure of the models, the ideas of sequential inclusion regressors are used, and for parametric identification, a modified method of least squares is used, adapted to work in a fuzzy environment. To determine the optimal temperature of the hydrotreating process on the basis of expert information and logical rules of conditional conclusions, rule bases are built. The constructed rule bases for determining the optimal temperature of the hydrotreating process depending on the thermal stability of the feedstock and the pressure in the hydrotreating furnace are implemented using the Fuzzy Logic Toolbox application of the MatLab package. Comparison results of data obtained with the known models, developed models and real, experimental data from the hydrotreating unit of the reforming unit are presented and the effectiveness of the proposed approach to modeling is shown.

Measuring Inconsistency over Sequences of Business Rule Cases

We investigate inconsistency and culpability measures for multisets of business rule bases. As companies might encounter thousands of rule bases daily, studying not only individual rule bases separately, but rather also their interrelations, becomes necessary. As current works on inconsistency measurement focus on assessing individual rule bases, we therefore present an extension of those works in the domain of business rules management. We show how arbitrary culpability measures (for single rule bases) can be automatically transformed for multisets, propose new rationality postulates for this setting, and investigate the complexity of central aspects regarding multi-rule base inconsistency measurement.

Deep Contrast Learning Approach for Address Semantic Matching

Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines.

Approximate reasoning with fuzzy rule interpolation: background and recent advances

Approximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.

A generic expert system framework with fuzzy Bayesian inference for medical classification and diagnosis in cardialogy [i.e. cardiology

Medical knowledge is expanding fast and it is difficult for general practitioners to remain abreast of all medical domains. Also, access to domain specialist is limited due to availability and geographical constraints. In many situations the diagnosis in [sic] upon the decision of the general practitioner and in cases this has resulted in the problem of patient's misdiagnosis. The purpose of this research is to create an expert system as a decision support model which is capable of risk analysis for diagnosis based on the patient's demography and laboratory tests. The expert system is designed in compliancy with medical communications protocol such as HL7 and can be integrated to any HL7 compliant Electronic Medical records system to provide more intelligence in diagnosis. Using linear scoring models and Fuzzy logic, the patient's demography and laboratory results will be used as rule bases. Such knowledge will be used as priors for a Bayesian engine to create the diagnostic spaces. Patient's information is compared in the space and the general practitioner can select between the possible hypotheses. Each diagnostic decision will be associated with a risk value. Using such scoring model provides a new semantic in diagnosis by providing risk values for every diagnosis made and by suggesting the most suitable treatment. Unlike many other existing expert systems, the architecture is designed in a generic standard which provides the capability to use the system for all medical domains. Achieving this generality has been a major goal achieved and its details are discussed in this document.

A generic expert system framework with fuzzy Bayesian inference for medical classification and diagnosis in cardialogy [i.e. cardiology

Medical knowledge is expanding fast and it is difficult for general practitioners to remain abreast of all medical domains. Also, access to domain specialist is limited due to availability and geographical constraints. In many situations the diagnosis in [sic] upon the decision of the general practitioner and in cases this has resulted in the problem of patient's misdiagnosis. The purpose of this research is to create an expert system as a decision support model which is capable of risk analysis for diagnosis based on the patient's demography and laboratory tests. The expert system is designed in compliancy with medical communications protocol such as HL7 and can be integrated to any HL7 compliant Electronic Medical records system to provide more intelligence in diagnosis. Using linear scoring models and Fuzzy logic, the patient's demography and laboratory results will be used as rule bases. Such knowledge will be used as priors for a Bayesian engine to create the diagnostic spaces. Patient's information is compared in the space and the general practitioner can select between the possible hypotheses. Each diagnostic decision will be associated with a risk value. Using such scoring model provides a new semantic in diagnosis by providing risk values for every diagnosis made and by suggesting the most suitable treatment. Unlike many other existing expert systems, the architecture is designed in a generic standard which provides the capability to use the system for all medical domains. Achieving this generality has been a major goal achieved and its details are discussed in this document.

An RVML extension for modeling fuzzy rule bases

Rules are still the most widespread way to represent expert knowledge despite the popularity of semantic technologies. The effective use of rules in decision-making in the case of inaccurate or uncertain information requires the development of specialized means and software for visual and generative programming. This paper considers an extension of the Rule Visual Modeling Language called FuzzyRVML designed for modeling fuzzy rule bases. FuzzyRVML supports a fuzzy datatype, concepts of a linguistic variable, terms, and certainty factors. The descriptions of FuzzyRVML basic elements, main constructions, and an illustrative example containing FuzzyCLIPS source code generation are presented. The evaluation and implementation of this notation are made based on the Personal Knowledge Base Designer software.