| 摘要 |
An artificial neural network (ANN) based system that is adapted to process an input pattern to generate an output pattern related thereto having a different number of components than the input pattern. The system ( 26 ) is comprised of an ANN ( 27 ) and a memory ( 28 ), such as a DRAM memory, that are serially connected. The input pattern ( 23 ) is applied to a processor ( 22 ), where it can be processed or not (the most general case), before it is applied to the ANN and stored therein as a prototype (if learned). A category is associated with each stored prototype. The processor computes the coefficients that allow the determination of the estimated values of the output pattern, these coefficients are the components of a so-called intermediate pattern ( 24 ). Assuming the ANN has already learned a number of input patterns, when a new input pattern is presented to the ANN in the recognition phase, the category of the closest prototype is output therefrom and is used as a pointer to the memory. In turn, the memory outputs the corresponding intermediate pattern. The input pattern and the intermediate pattern are applied to the processor to construct the output pattern ( 25 ) using the coefficients. Typically, the input pattern is a block of pixels in the field of scaling images.
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