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Instant conversation applied sciences proceed to endure swift development. The reputation of instant Mesh Networks (WMN)s, ordinarily, could be attributed to their features: the power to dynamically self-organize and self-configure, coupled being able to keep mesh connectivity, leads in influence to low set-up/installation charges, easier upkeep projects, and repair insurance with excessive reliability and fault-tolerance.
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Contemporary years have obvious a development in strategic alliances, mergers and acquisitions and collaborative networks concerning knowledge-intensive and hi-tech industries. notwithstanding, there were particularly few reports taking a look at this manner of collaboration as a method to force organizations’ cutting edge performances.
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Extra resources for Artificial neural networks - industrial and control engineering applications
Textile Research Journal, 2009, 79(5), 468-478. S. G. Computer Vision-Aided Fabric Inspection System for On-Circular Knitting Machine. Textile Research Journal, 2005, 75(6), 492-497. , Youssef, S. and Pastore, C. Detection and Classification of Defects in Knitted Fabric Structures. Textile Research Journal, 2006, 76(4), 295-300. , Nahavandi, S. Z. Intelligent Animal Fiber Classification with Artificial Neural Networks. Textile Research Journal, 2002, 72(7), 594-600. S. S. Classifying Web Defects with a Back-Propagation Neural Network by Color Image Processing.
Wang, X. and Beltran, R. An Artificial Neural Networkbased Hairiness Prediction Model for Worsted Wool Yarns. Textile Research Journal, 2009, 79(8), 714-720. I. S. Using Neural Network Theory to Predict the Properties of Melt Spun Fibers. Textile Research Journal, 2004, 74(9), 840-843. J. Prediction of Yarn Shrinkage using Neural Nets. Textile Research Journal, 2007, 77(5), 336-342. G. S. An Artificial Neural Network Model for the Prediction of Spirality of Fully Relaxed Single Jersey Fabrics.
The number of the nodes was set as 10 by many experiments. The training function of the neural network was a gradientdescending method based on momentum and an adaptive learning rate. The learning function of connection weights and threshold values was a momentum-learning method based on gradient descending. Twenty two images of each class were used as training samples and the other ten images were testing samples. , 2009). 3.