В данной статье решается задача построения нейронечеткой модели формирования нечетких правил и их использования для оценки состояния объектов в условиях неопределенности. Традиционные методы математической статистики или имитационного моделирования не позволяют строить адекватные модели объектов в указанных условиях. Поэтому в настоящее время решение многих задач основано на использовании технологий интеллектуального моделирования с применением методов нечеткой логики. Традиционный подход к построению нечетких систем связан с необходимостью привлечения эксперта для формулирования нечетких правил и задания используемых в них функций принадлежности. Для устранения этого недостатка актуальна автоматизация формирования нечетких правил на основе методов и алгоритмов машинного обучения. Одним из подходов к решению данной задачи является построение нечеткой нейронной сети и обучение ее на данных, характеризующих исследуемый объект. Реализация этого подхода потребовала выбора вида нечетких правил с учетом особенностей обрабатываемых данных. Кроме того, потребовалась разработка алгоритма логического вывода на правилах выбранного вида. Этапы алгоритма определяют число слоев в структуре нечеткой нейронной сети и их функциональность. Разработан алгоритм обучения нечеткой нейронной сети. После ее обучения производится формирование системы нечетко-продукционных правил. На базе разработанного математического обеспечения реализован программный комплекс. На его основе проведены исследования по оценке классифицирующей способности формируемых нечетких правил на примере анализа данных из UCI Machine Learning Repository. Результаты исследований показали, что классифицирующая способность сформированных нечетких правил не уступает по точности другим методам классификации. Кроме того, алгоритм логического вывода на нечетких правилах позволяет успешно производить классификацию при отсутствии части исходных данных. С целью апробации произведено формирование нечетких правил для решения задачи по оценке состояния водоводов в нефтяной отрасли. На основе исходных данных по 303 водоводам сформирована база из 342 нечетких правил. Их практическая апробация показала высокую эффективность в решении поставленной задачи.

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Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty

This article considers the task of objects state assessing in conditions of uncertainty by considering the supply chain strategy. To solve it, the need to use fuzzy-production knowledge bases and fuzzy inference algorithms as part of fuzzy decision support systems is being updated. As a tool for constructing a knowledge base, a neural-fuzzy model is proposed. The proposed type of fuzzy-production rules and the logic inference algorithm on rules for objects state assessing are described. A structure of a fuzzy neural network, consisting of six layers, each of which implements the corresponding stage of the logic inference algorithm, is proposed. As a result of training a fuzzy neural network, a system of fuzzy-production rules is formed, which make up the knowledge base of the decision support system for objects state assessing. On the basis of the proposed neuro-fuzzy model, a software package has been implemented for automating the processes of forming fuzzy-production rules. The main components of the software package are the knowledge base generation module and the fuzzy inference module. As an approbation of the neuro-fuzzy model, the formation of fuzzy rules for assessing the state of water lines at the cluster pumping stations in reservoir pressure maintenance systems has been carried out. The testing results confirmed the high efficiency of the neural-fuzzy model and the possibility of its practical use for the formation of fuzzy-production rules in various subject areas of human activity.

https://ojs.excelingtech.co.uk/index.php/IJSCM/article/download/3835/1902

Neuro-Fuzzy Model in Supply Chain Management for Objects State Assessing

/pdf/neural-network-model-of-human-intoxication-functional-state-1fj8ibrgmm.pdf

Neural network model of human intoxication functional state determining in some problems of transport safety solution

In this paper, a stability analysis strategy of nonlinear control systems is proposed. An adaptive neural control scheme composed of an emulator, and a controller with decoupled adaptive rates is considered. A Lyapunov function based on tracking error dynamic is retained and an online adjusting technique of the neural controller adaptive rate is adopted to improve the closed loop performance in terms of stability, rapidity and precision. Comparative studies and an experimental validation on a semi-batch reactor are realized to prove the efficiency of the developed strategy.

Stability Analysis Strategy for the Adaptive Neural Control System: A Practical Validation Via a Transesterification Reactor

This paper describes the technology of neural network application to solve the problem of information security incidents forecasting. We describe the general problem of analyzing and predicting time series in a graphical and mathematical setting. To solve this problem, it is proposed to use a neural network model. To solve the task of forecasting a time series of information security incidents, data are generated and described on the basis of which the neural network is trained. We offer a neural network structure, train the neural network, estimate it's adequacy and forecasting ability. We show the possibility of effective use of a neural network model as a part of an intelligent forecasting system.

Neural Network Model for Information Security Incident Forecasting

This paper solves the task of complex objects approximation with a discrete output based on information approach to modeling. We propose a model of fuzzy rules and the inference algorithm on the rules, and describe the neuro-fuzzy model for generation of a knowledge base. The approximation of known data sets and comparison of the results with those of other authors is performed. Examples of knowledge bases generation of the expert diagnostic systems in medicine, oil industry and information security show effectiveness of the proposed approach.

Neuro-fuzzy model of complex objects approximation with discrete output

The article discusses the problem of constructing a neural network model of the regulator for controlling a technical object with a monotonous smooth behavior that does not have a mathematical description. To solve this problem, we propose a method of construction of a technical object inverse model in the form of a neural emulator - perceptron. In this article we describe the algorithm for determining a number of the intermediate layers of the perceptron and the values of its weights, also the method of forming the training samples on the basis of knowledge of the only known required behaviors of the object without carrying out experimental studies. The construction of neural networks is performed using the nntool package in the Matlab environment. The validation of neural networks is carried out using the model of the automatic control system in the environment of visual modeling Simulink (Matlab). Finally, we consider the results of the suggested methods by the example of a one-dimensional control of the roll angle of an unmanned aerial vehicle.

L.Yu. Emaletdinova

Papers

Neuro-fuzzy model of complex objects approximation with discrete output

Development of Neural Network Model of Regulator for Automatic Control System of Technical Object in Absence of Mathematical Model of Object