The Research Of Color Recognition Based On Elman Neural Network And Clustering Algorithm

The measurement and control of color especially the assessment of aberration are problems often encountered in scientific research and production. In order to adapt to the needs of the actual industrial production, it is required that the color of the sample colors should be accurately measured and the aberration should be evaluated in such a scientific way that the evaluation result can reflect more appropriately the visual differences between samples, helping to judge correctly the color quality of products and control the productive process scientifically and efficiently.This paper make a systematic study of color identification in a way that combines OPB780 color sensor with Elman neural network and clustering algorithm. The details are as follows:(1)This paper describes the relationship between objects and colors, theory of relative colors, as well as the principles for color measurement, it also discusses the conversion between the color tristimulus values and chroma values and the exchange from RGB color space to CMYK color space.(2) Through the analysis of OPB780's color sensing principle and features, we designed external auxiliary measurement circuit with AT89C51 microcontroller as the core measurement unit; After taking into account the measured data show rapid and stable, designed to PL2303 chip circuits as the core of a virtual serial port computer display program.(3) We designed light compensation circuit and data collection chamber according to the OPB780 sensor photosensitivity color and programmed the Keil C51, software for color measurement, based on the white balance theory and the principle of frequency measurement of microcontroller. We have measured the R, G, B, C frequency of the 2200 training samples and 39 test samples of color card by the circuit and the software.(4) We proposed the methods for color recognition based on the memory function of Elman neural network. After the normalization of input data, and several adjustment of test network node, we designed the best network structure. Take the improved K-means clustering algorithm: calculating the color difference between target color and 8 common colors, i.e. black, blue, red, violet, blue, green, yellow and white, by use of CIE 1976L *a * b * color difference formula we proposed the evaluation criteria of clustering algorithms, that is , one color belongs to one of the common colors with which it has a minimum difference.The research results speak for the effects of Elman neural network.