The process of eliminating color errors from the gamut mismatch, resolution conversion, quantization and nonlinearity between scanner and printer is usually recognized as an essential issue of color reproduction. To efficiently calibrate the nonlinearity between scanning/printing devices, we present a linear systolic array architecture to realize the higher-order CMAC neural network model and propose an extended direct weight cell address mapping scheme for weight retrieving. This mapping scheme exhibits fast computation speed in generating weight cell addresses. Some experiments are performed to evaluate the approximation capability of the higher-order CMAC neural network models. It is shown that the CMAC model behaves well for those trained regions over the input space and exhibits smooth approximation for those untrained regions over the input space.
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