<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mahdaoui Rafik</style></author><author><style face="normal" font="default" size="100%">Mouss Leila Hayet</style></author><author><style face="normal" font="default" size="100%">Mouss Med Djamel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems, May, ISSN 1694-0814</style></title><secondary-title><style face="normal" font="default" size="100%">IJCSI International Journal of Computer Science Issues</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/51917361_A_Temporal_Neuro-Fuzzy_Monitoring_System_to_Manufacturing_Systems</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">Vol 8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.</style></abstract><issue><style face="normal" font="default" size="100%"> Issue 3</style></issue></record></records></xml>