Scope and potential of applying artificial neural networks in reliability prediction with a focus on railway rolling stock

2011 ◽  
pp. 508-514 ◽  
Author(s):  
O Fink ◽  
U Weidmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reliability Prediction ◽  
Rolling Stock ◽  
Artificial Neural ◽  
Railway Rolling Stock

AN EFFECTIVE EARLY SOFTWARE RELIABILITY PREDICTION PROCEDURE FOR PROCESS ORIENTED DEVELOPMENT AT PROTOTYPE LEVEL EMPLOYING ARTIFICIAL NEURAL NETWORKS

2011 ◽  
Vol 18 (03) ◽  
pp. 237-250 ◽  
Author(s):  
K. KRISHNA MOHAN ◽  
A. K. VERMA ◽  
A. SRIVIDYA
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Reliability ◽  
Heterogeneous Systems ◽  
Reliability Prediction ◽  
Feed Forward Neural Network ◽  
Distributed Components ◽  
Software Reliability Prediction ◽  
Artificial Neural ◽  
Process Oriented

Reliability of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. In this context, reliability prediction of software process oriented systems assumes prime importance. It is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems while building distributed based applications. This paper presents an algorithm using feed-forward neural network for early qualitative software reliability prediction. The inputs for neural networks consist of techno-complexity, practitioner's level, creation effort, size and leakage defects. The number of defects detected in each cycle can be predicted by using Artificial Neural Networks (ANN). Illustrative examples prove the effectiveness of the methodology employed.

scholarly journals Operational Rationing of Energy Resources for Train Traction using the Method of Artificial Neural Networks

2020 ◽  
Vol 18 (1) ◽  
pp. 158-169
Author(s):  
S. V. Malakhov ◽  
M. Yu. Kapustin
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Resources ◽  
Rolling Stock ◽  
Dynamic Programming Method ◽  
Regulation System ◽  
Energy Regulation ◽  
Artificial Neural

The task of resource saving is relevant for all transport companies and many world communities of scientists and engineers are engaged in search for ways to solve it. World railway companies, and especially large ones such as Russian Railways, are large consumers of energy resources and the problem of saving is the most urgent for them. One of directions for solving this task can be the use of an optimal energy regulation system for traction of each train – an operational rationing system. Such a task can only be solved by modelling the process of movement through the dynamic programming method. In modern conditions of development of engineering and technology, it has become possible to develop such operational standardization systems endowed with important properties: high performance, multitasking, solution accuracy, ease of use and maintenance. These requirements impose certain restrictions on the architecture of the operational rationing system. Typical system architecture should be built around a centralized node, which will act as a solver and storage, nodes for input and output of information can be geographically separated. The method of dynamic programming can be improved by using it in the process of training artificial neural networks, which will form not only a priori estimates of energy consumption for traction, but also an a posteriori estimate of train control (by a train driver or auto-driving system). Also, the use of artificial neural networks will allow us to continuously improve the method due to training using the accumulated amount of data from real trips, which will allow us to clarify the norms of energy consumption and to plan our costs in the future. The prototype of the operational standardization system was developed at the department of traction rolling stock of Russian Open Academy of Transport of Russian University of Transport and the results obtained allow us to state that the chosen approach to solving the problem of energy saving has been chosen correctly.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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