An approach for linguistic multi-attribute decision making based on linguistic many-valued logic

There are various types of multi-attribute decision-making (MADM) problems in our daily lives and decision-making problems under uncertain environments with vague and imprecise information involved. Therefore, linguistic multi-attribute decision-making problems are an important type studied extensively. Besides, it is easier for decision-makers to use linguistic terms to evaluate/choose among alternatives in real life. Based on the theoretical foundation of the Hedge algebra and linguistic many-valued logic, this study aims to address multi-attribute decision-making problems by linguistic valued qualitative aggregation and reasoning method. In this paper, we construct a finite monotonous Hedge algebra for modeling the linguistic information related to MADM problems and use linguistic many-valued logic for deducing the outcome of decision making. Our method computes directly on linguistic terms without numerical approximation. This method takes advantage of linguistic information processing and shows the benefit of Hedge algebra

Developing a Model of an Intelligent Control Technique on a Manufacturing Batching Process

Complex control algorithms are applied to manufacturing systems for certain process requirements, according to product specifications. When implementing specific complex control algorithms, primary and secondary conditions affect each other, affecting the measuring and control processes. While complex control algorithms result in several benefits, problems associated with mathematical reasoning and time delays need to be considered for an intelligent decision-making control technique to optimise control of the manufacturing process. The research will derive a suitable control technique by means of an adaptive neuro-fuzzy inference system, to optimise the manufacturing process. The paper will discuss technical aspects, the experimental set-up and the design process. Completed research on industrial Siemens FuzzyControl++ design tool and current research on MatLab Fuzzy Logic Toolbox will form part of the discussion on the design process. The paper will conclude with a comparison of various analysis results in MatLab Fuzzy Logic Toolbo

Побудова класифікаційної нечіткої бази знань на основі трендових правил і оберненого виведення

In this paper, an approach to fuzzy classification rules construction within the framework of fuzzy relation equations is proposed. At the same time, the system of fuzzy trend rules serves as a carrier of expert information and generator of rules - solutions of fuzzy relation equations. The system of fuzzy classification rules can be rearranged as a set of linguistic solutions of fuzzy relation equations using the composite system of fuzzy terms, e.g. “significant rise”, “essential drop” etc., where causes and effects significance measures are described by fuzzy quantifiers. The problem of inverse logical inference, which lies in restoring the coordinates of the maximum of the fuzzy input terms membership functions for each output class is reduced to solving the system of fuzzy relation equations using a genetic algorithm.The proposed approach allows to avoid the alternative rule selection. The aim of the rule selection methods is to reduce the system complexity by removing inefficient and redundant rules and improve the system accuracy by introducing alternative rules into the final rule base. Using expert knowledge cannot guarantee the optimal cooperation activity among rules. The rule selection problem is still relevant since there is currently no single methodical standard for the optimal structural adjustment of fuzzy classification knowledge bases.Solving fuzzy relation equations using the genetic algorithm ensures the optimal number of fuzzy rules for each output term and optimal form of the membership functions of the fuzzy input terms for each linguistic solution.Consecutive solution of the optimization problems provides complexity reduction of the problem of fuzzy classification knowledge bases generation. Предложен метод построения классификационных нечетких баз знаний, в которых носителем экспертной информации являются трендовые правила «причины - следствия». Показано, что классификационные нечеткие правила, которые связывают меры значимостей причин и следствий с помощью нечетких квантификаторов, представляют множество решений системы нечетких логических уравнений для заданных классов выхода.Запропоновано метод побудови класифікаційних нечітких баз знань, в яких носієм експертної інформації є трендові правила «причини - наслідки». Показано, що класифікаційні нечіткі правила, які з’єднують міри значимостей причин і наслідків за допомогою нечітких квантифікаторів, представляють множину розв’язків системи нечітких логічних рівнянь для заданих класів виходу

Побудова класифікаційної нечіткої бази знань на основі трендових правил і оберненого виведення

In this paper, an approach to fuzzy classification rules construction within the framework of fuzzy relation equations is proposed. At the same time, the system of fuzzy trend rules serves as a carrier of expert information and generator of rules - solutions of fuzzy relation equations. The system of fuzzy classification rules can be rearranged as a set of linguistic solutions of fuzzy relation equations using the composite system of fuzzy terms, e.g. “significant rise”, “essential drop” etc., where causes and effects significance measures are described by fuzzy quantifiers. The problem of inverse logical inference, which lies in restoring the coordinates of the maximum of the fuzzy input terms membership functions for each output class is reduced to solving the system of fuzzy relation equations using a genetic algorithm.The proposed approach allows to avoid the alternative rule selection. The aim of the rule selection methods is to reduce the system complexity by removing inefficient and redundant rules and improve the system accuracy by introducing alternative rules into the final rule base. Using expert knowledge cannot guarantee the optimal cooperation activity among rules. The rule selection problem is still relevant since there is currently no single methodical standard for the optimal structural adjustment of fuzzy classification knowledge bases.Solving fuzzy relation equations using the genetic algorithm ensures the optimal number of fuzzy rules for each output term and optimal form of the membership functions of the fuzzy input terms for each linguistic solution.Consecutive solution of the optimization problems provides complexity reduction of the problem of fuzzy classification knowledge bases generation. Предложен метод построения классификационных нечетких баз знаний, в которых носителем экспертной информации являются трендовые правила «причины - следствия». Показано, что классификационные нечеткие правила, которые связывают меры значимостей причин и следствий с помощью нечетких квантификаторов, представляют множество решений системы нечетких логических уравнений для заданных классов выхода.Запропоновано метод побудови класифікаційних нечітких баз знань, в яких носієм експертної інформації є трендові правила «причини - наслідки». Показано, що класифікаційні нечіткі правила, які з’єднують міри значимостей причин і наслідків за допомогою нечітких квантифікаторів, представляють множину розв’язків системи нечітких логічних рівнянь для заданих класів виходу