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- W2027367059 abstract "This paper presents the design of an artificial neural network, Cabd a “neC%OQniVOn”, for seismic pattern recognitbn. The neocognftton was proposed by Fukushima and is quite important in handwritten numerals. A seismic pattern recognition system which works with the mechanism of the neocognitron is discussed in this paper to demonstrate the ability of the neocognitmn which can overcome the difficulty with the seismic patterns being distorted in shape, changed in size, and shiied in position. The system has been trained using a supervised classification. The neocognitron is a hierarchical multilayered network consisting of a cascade of many layers of neuron-like cells of the anafog type, and has variable connections between the cells in adjoining layers. It can acquire the ability to recognize patterns by training. During the training process, “teacher Presents a Set of training patterns and points out which cells should be the seed cells for each training patterns. Training of each subsequent layer does not begin until the training of the previous layer has finished. After finishing the process of learning, pattern recognition is performed on the basis of similarfly in shape between patterns, and not affected by the deformation. Shift invariance, scale invariance and noise tolerance can be achieved through a proper choice of design parameter. In the neccognitron, local features of the input pattern are extracted by Ihe cells of fewer stage, and gradually integrated into more global feature. Finally, each cell of the highest stage integrates all the information of the input pattern, and responds only to one pattern. In our experiment, Ihe neccognitron has successfully recognized the distorled input patterns, and correct classification could be maintained with up to 10% of the input pixels mrmpted." @default.
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- W2027367059 modified "2023-09-27" @default.
- W2027367059 title "Neocognitron of a neural network for seismic pattern recognition" @default.
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- W2027367059 doi "https://doi.org/10.1190/1.1822062" @default.
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