Color coordination system on case based reasoning system using neural network

We propose a case-based reasoning (CBR) system whose case database consists of a neural network, and describe its application to a color coordination system. Furthermore, we apply a neural network to the CBR system to deal with fuzzy values which represent colors. The neural network in the CBR system, however, must learn a new case incrementally without forgetting the learned instances. To realize this ability, we use a new incremental learning method proposed by the us to reduce the computational complexity for the learning. In the new method, the system learns the generalized radial cases function both the new case and some old cases that are predicted and being interfered by the learning. For the experiments, we constructed a color coordination system for a make-up around eyes. The result gave appropriate combinations of colors that satisfied the user.