Assessment of Transmission Line Condition Based on Neural Networks and Fuzzy Logic Decision

2014 ◽  
Vol 687-691 ◽  
pp. 3137-3140
Author(s):  
Jin Ming Yao ◽  
Jun Jie Yang ◽  
Zhi Bin Lou
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Transmission Line ◽  
Accuracy Assessment ◽  
Assessment Model ◽  
Stable Operation ◽  
Output State ◽  
Current State ◽  
Convergence Line ◽  
Timely Warning

Accurate assessment of the operational status of transmission line,and the line status timely warning, can protect the stable operation of the transmission line. Not high accuracy assessment for the current state of the transmission line,assessment model of transmission line state based on neural networks and fuzzy logic decisions are put forward,built on online monitoring system, Considering the conductor temperature, angle, tension and other parameters.Using BP neural network convergence line fault membership values, and the output state is line fault probability. Then ,the uncertainty of the state probability output line integrated assessmen through Fuzzy Reasoning.

scholarly journals Risk assessment model of investment business process in the forest industry

2020 ◽  
pp. 271-278
Author(s):  
Л.А. Чудовская ◽  
С.М. Галилеев ◽  
М.М. Галилеев
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Business Process ◽  
Assessment Model ◽  
Investment Efficiency ◽  
Risk Assessment Model ◽  
Timber Industry ◽  
Current State ◽  
State Of The Economy ◽  
Matlab Package

Рассмотрена оценка инвестиционного бизнес-процесса в отрасли лесопромышленного комплекса с помощью аппарата нечеткой логики. Модель позволяет оптимизировать работу транспортных узлов лесного комплекса, работу деревообрабатывающих предприятий, леспромхозов с точки зренияэффективности вложения инвестиций в условиях неопределенности современного состояния экономики. Рассмотрена модель оценки риска как задачи линейного программирования в нечеткой постановке. Использованы построенные функции принадлежности - треугольные и гауссовы на основе экспертных оценок. При решении задачи использован пакет MatLab с Toolbox Fuzzy Logic и метод Балаша. Полученные результаты показали работоспособность предлагаемой модели и возможность использования ее в различных отраслях лесопромышленного комплекса к оценке риска инвестиционного бизнес-процесса. The article considers the evaluation of the investment business process in the timber industry using fuzzy logic. The model makes it possible to optimize the operation of transport nodes of the forest complex, the work of woodworking enterprises, forestry enterprises in terms of investment efficiency in the conditions of uncertainty of the current state of the economy. The risk assessment model is considered as a linear programming problem in a fuzzy formulation. The constructed membership functions - triangular and Gaussian-based on expert estimates are used. The MatLab package with Toolbox Fuzzy Logic and the Balash method were used to solve the problem. The results obtained showed the efficiency of the proposed model and the possibility of using it in various sectors of the timber industry to assess the risk of investment business process.

Assessment of Transmission Line Icing State Based on Multi-Sensor Information Fusion

2014 ◽  
Vol 687-691 ◽  
pp. 3141-3144
Author(s):  
Jin Ming Yao ◽  
Jun Jie Yang ◽  
Zhi Bin Lou
Keyword(s):  
Transmission Line ◽  
Monitoring System ◽  
Information Fusion ◽  
Computational Models ◽  
Meteorological Parameters ◽  
Output State ◽  
Convergence Line ◽  
Sensor Information ◽  
Membership Value ◽  
Current Monitoring

Due to considerations limited for the current monitoring techniques and computational models and other reasons,the accuracy rate of line icing condition assessment is not high. Transmission line icing are affected by many factors, having greater relevance with micro-meteorological parameters. To improve the assessment accuracy of transmission line icing condition,multi-sensor information fusion method are put forward for a comprehensive assessment to Line icing state, based on online monitoring system,considering the equivalent ice thickness of monitoring system, micro-meteorological parameters and duration of ice cover.BP neural network convergence line icing membership value, the output state is cing probability. Then ,the probability of the state of uncertainty output line integrated assessmen through Fuzzy Reasoning

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Withdrawn: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC)

2019 ◽  
Vol 13 ◽  
Author(s):  
Abeer A. Amer ◽  
Soha M. Ismail
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Recent Advances ◽  
Vascular Center ◽  
Copyright Permission ◽  
Legal Right

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

scholarly journals A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 23
Author(s):  
Duy-Anh Nguyen ◽  
Xuan-Tu Tran ◽  
Francesca Iacopi
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Training Algorithms ◽  
Current State ◽  
Hardware Implementations ◽  
Neuromorphic Hardware ◽  
High Level ◽  
Event Based ◽  
Hardware Platforms

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

scholarly journals Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model

2021 ◽  
Vol 13 (2) ◽  
pp. 832
Author(s):  
Aleksandar Blagojević ◽  
Sandra Kasalica ◽  
Željko Stević ◽  
Goran Tričković ◽  
Vesna Pavelkić
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Assessment Model ◽  
Level Crossings ◽  
Fuzzy Matrix ◽  
Sustainable Performance ◽  
Current State ◽  
Criterion Weight ◽  
Railway System ◽  
Criteria Importance

Sustainable traffic system management under conditions of uncertainty and inappropriate road infrastructure is a responsible and complex task. In Bosnia and Herzegovina (BiH), there is a large number of level crossings which represent potentially risky places in traffic. The current state of level crossings in BiH is a problem of the greatest interest for the railway and a generator of accidents. Accordingly, it is necessary to identify the places that are currently a priority for the adoption of measures and traffic control in order to achieve sustainability of the whole system. In this paper, the Šamac–Doboj railway section and passive level crossings have been considered. Fifteen different criteria were formed and divided into three main groups: safety criteria, road exploitation characteristics, and railway exploitation characteristics. A novel integrated fuzzy FUCOM (full consistency method)—fuzzy PIPRECIA (pivot pairwise relative criteria importance assessment) model was formed to determine the significance of the criteria. When calculating the weight values of the main criteria, the fuzzy Heronian mean operator was used for their averaging. The evaluation of level crossings was performed using fuzzy MARCOS (measurement of alternatives and ranking according to compromise solution). An original integrated fuzzy FUCOM–Fuzzy PIPRECIA–Fuzzy MARCOS model was created as the main contribution of the paper. The results showed that level crossings 42 + 690 (LC4) and LC8 (82 + 291) are the safest considering all 15 criteria. The verification of the results was performed through four phases of sensitivity analysis: resizing of an initial fuzzy matrix, comparative analysis with other fuzzy approaches, simulations of criterion weight values, and calculation of Spearman’s correlation coefficient (SCC). Finally, measures for the sustainable performance of the railway system were proposed.

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