A study on the robustness of adaptive pattern recognition

A color-model-based control of a nonlinear system with significant light's disturbance effects for an image process problem is proposed. First, a design methodology based on the Lyapunov analysis is presented. Second, the scheme is composed with an adaptive control part of the neurons controller with error effects, and a supervisory control part to enhance robustness against LED light disturbances and image model uncertainties. Third, an effective supervised adaptive control theory is used to tackle the image identification problem. Experimental results with a Kinect image sensor are obtained from a practical marker identification system, and they show that the proposed image identification technique has excellent performance when it is compared with the traditional image process method. Also, the feed-forward term of photoresistor is able to provide extra improvement in the image identification.