scholarly journals Neural Networks and Fault Probability Evaluation for Diagnosis Issues

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yahia Kourd ◽  
Dimitri Lefebvre ◽  
Noureddine Guersi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Probabilistic Methods ◽  
Fault Detection And Isolation ◽  
Fault Probability ◽  
Confidence Factor ◽  
Probability Evaluation ◽  
Quantitative Indicators ◽  
Probabilistic Criteria

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

scholarly journals FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS

2019 ◽  
Vol 26 ◽  
pp. 36-46
Author(s):  
S. KONOVALOV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Technical System ◽  
Bulletin Board ◽  
Confidence Factor ◽  
Complex Technical System ◽  
Artificial Neural

In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

Real-Time Recoloring Ishihara Plates Using Artificial Neural Networks for Helping Colorblind People

2020 ◽  
pp. 138-156
Author(s):  
Martín Montes Rivera ◽  
Alejandro Padilla ◽  
Juana Canul-Reich ◽  
Julio Ponce
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Input Data ◽  
Traffic Signals ◽  
Educational Materials ◽  
Ishihara Test ◽  
Artificial Neural ◽  
Intelligence Technique

Vision sense is achieved using cells called rods (luminosity) and cones (color). Color perception is required when interacting with educational materials, industrial environments, traffic signals, among others, but colorblind people have difficulties perceiving colors. There are different tests for colorblindness like Ishihara plates test, which have numbers with colors that are confused with colorblindness. Advances in computer sciences produced digital assistants for colorblindness, but there are possibilities to improve them using artificial intelligence because its techniques have exhibited great results when classifying parameters. This chapter proposes the use of artificial neural networks, an artificial intelligence technique, for learning the colors that colorblind people cannot distinguish well by using as input data the Ishihara plates and recoloring the image by increasing its brightness. Results are tested with a real colorblind people who successfully pass the Ishihara test.

Mathematical Modeling of Artificial Neural Networks

2011 ◽  
pp. 1056-1063
Author(s):  
Radu Mutihac
Keyword(s):  
Mathematical Modeling ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Neuroscience ◽  
Distributed Processing ◽  
Neural Modeling ◽  
Cognitive Systems ◽  
Neural Computing ◽  
Alternative Means

Models and algorithms have been designed to mimic information processing and knowledge acquisition of the human brain generically called artificial or formal neural networks (ANNs), parallel distributed processing (PDP), neuromorphic or connectionist models. The term network is common today: computer networks exist, communications are referred to as networking, corporations and markets are structured in networks. The concept of ANN was initially coined as a hopeful vision of anticipating artificial intelligence (AI) synthesis by emulating the biological brain. ANNs are alternative means to symbol programming aiming to implement neural-inspired concepts in AI environments (neural computing) (Hertz, Krogh, & Palmer, 1991), whereas cognitive systems attempt to mimic the actual biological nervous systems (computational neuroscience). All conceivable neuromorphic models lie in between and supposed to be a simplified but meaningful representation of some reality. In order to establish a unifying theory of neural computing and computational neuroscience, mathematical theories should be developed along with specific methods of analysis (Amari, 1989) (Amit, 1990). The following outlines a tentatively mathematical-closed framework in neural modeling.

ANN Application in the Field of Structural Concrete

2011 ◽  
pp. 118-124
Author(s):  
Juan L. Pérez ◽  
Mª Isabel Martínez ◽  
Manuel F. Herrador
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resistance ◽  
Structural Engineering ◽  
Construction Material ◽  
Fabrication Process ◽  
Structural Concrete ◽  
Cost Efficient ◽  
Ann Application

Artificial Intelligence (AI) mechanisms are more and more frequently applied to all sorts of civil engineering problems. New methods and algorithms which allow civil engineers to use these techniques in a different way on diverse problems are available or being made available. One AI techniques stands out over the rest: Artificial Neural Networks (ANN). Their most remarkable traits are their ability to learn, the possibility of generalization and their tolerance towards mistakes. These characteristics make their use viable and cost-efficient in any field in general, and in Structural Engineering in particular. The most extended construction material nowadays is concrete, mainly because of its high resistance and its adaptability to formwork during its fabrication process. Along this chapter we will find different applications of ANNs to structural concrete.

scholarly journals Application of ANN in Milling Process: A Review

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Problems ◽  
Milling Process ◽  
Machining Processes ◽  
Traditional Methods ◽  
Artificial Neural

In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

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