Color reproduction is a complex nonlinear mapping problem due to gamut mismatch, resolution conversion, quantization, nonlinear color relationship between scanner and printer. To solve such a complex problem in color reproduction, this paper proposes a fuzzy CMAC model, which adopts a special parallel fuzzy inference-like process to realize the function similar to higher-order CMAC. In this model, recursive B-spline receptive field functions are replaced by fuzzy sets with bell-shaped membership function, and the weights to evaluate output values are also not crisp values but fuzzy sets. The learning algorithm is based on the maximum gradient method. For the situations of insufficiently or irregularly distributed training patterns, this paper develops a sampling method to generate uniformly distributed training patterns. According to experimental results, the proposed fuzzy CMAC model has shown its effectiveness on color reproduction and general function approximations. Besides, it has advantages of fast learning speed, simple computation, and high stability on model parameters.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence