J. Jiang, Algorithm design of an image compression neural network, in: Proc. World Congress on Neural Networks, Washington, DC, 17}21 July 1995, pp. I792}I798, ISBN: 0-8058-2125. Home/Search Document Not in Database Summary Related Articles Check |
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Quasi-Four-Dimensional-Neuroncube and its Application to.. - Seiffert, Michaelis (1998) (1 citation) (Correct)
....Often the well known procedures using correlation functions or tracking algorithms to determine the movement of single or compounded objects are used. In most cases some kind of pre processing has to be performed. Some examples of existing implementations using neural networks are given in [5] [7], 14] 15] However, these results can be improved once more by using a combination of several parts of Soft Computing. 2 Quasi Four Dimensional Neuroncube (QFDN) The Quasi Four Dimensional Neuroncube (QFDN) is an extension of the Self Organizing Maps (SOM) 8] It compounds an artificial ....
J. Jiang, "Algorithm design of an image compression neural network," in Proc. World Congress on Neural Networks, vol. 1, pp. 792-798, 1995.
Image compression with neural networks - A survey - Jiang (1999) (1 citation) Self-citation (Jiang) (Correct)
.... this general structure, various learning algorithms have been designed and developed such as Kohonen s self organizing feature mapping [10,13,18,33,52,70] competitive learning [1,54,55,65] frequency sensitive competitive learning [1,10] fuzzy competitive learning [11,31,32] general learning [25,49], and distortion equalized fuzzy competitive learning [7] and PVQ (predictive VQ) neural networks [46] Let W (t) be the weight vector of the ith neurone at the tth iteration, the basic competitive learning algorithm can be summarized as follows: z 1 d(x, t) min d(x, ....
....up the training process by optimizing the search for the winner and reducing the relevant computing costs [29] By considering general optimizing methods [19] numerous variations of learning algorithms can be designed for the same neural network structure shown in Fig. 5. The general learning VQ [25,49] is one typical example. To model the optimization of centroids w3R# for a xed set of training vectors x #x , 2 , x # #, a total average mismatch between x, the input vector, and W, the code book represented by neurone weights, is de ned as #( ## #x # , 2.21) where g is ....
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J. Jiang, Algorithm design of an image compression neural network, in: Proc. World Congress on Neural Networks, Washington, DC, 17}21 July 1995, pp. I792}I798, ISBN: 0-8058-2125.
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