Prediction of color simulation prescription for traditional Chinese medicine placebo solution based on whale algorithm-optimized back propagation neural network
Traditional Chinese medicine(TCM)placebos are simulated preparations for specific objects and the color simulation in the development of TCM placebos is both crucial and challenging.Traditionally,the prescription screening and pattern exploration process involves extensive experimentation,which is both time-consuming and labor-intensive.Therefore,accurate prediction of color simulation prescriptions holds the key to the development of TCM placebos.In this study,we efficiently and precisely predict the color simulation prescriptions of placebos using an image-based approach combined with Matlab software.Firstly,images of TCM placebo solutions are captured,and 13 chromaticity space values such as the L∗a∗b∗,RGB,HSV,and CMYK values are extracted using Photoshop software.Correlation analysis and normalization are then performed on these extracted values to construct a 13×9×3 back propagation(BP)neural network model.Subsequently,the whale optimization algorithm(WOA)is employed to optimize the initial weights and thresholds of the BP neural network.Finally,the optimized WOA-BP neural network is validated using three representative instances.The training and prediction results indicate that,compared to the BP neural network,the WOA-BP neural network demonstrates superior performance in predicting the pigment ratios of placebos.The correlation coefficients for training,validation,testing,and the overall dataset are 0.95,0.87,0.95,and 0.95,respectively,approaching unity.Furthermore,all error values are reduced,with the maximum reduction reaching 99.83%.The color difference(ΔE)values for the three validation instances are all less than 3,further confirming the accuracy and practicality of the WOA-BP neural network approach.
traditional Chinese medicine(TCM)placebocolor simulationback propagation(BP)neural networkchromaticity space valueswhale algorithm
NETL
NSTL
万方数据
