tailieunhanh - Artificial Neural Networks Industrial and Control Engineering Applications Part 3

Tham khảo tài liệu 'artificial neural networks industrial and control engineering applications part 3', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Artificial Neural Network Prosperities in Textile Applications 59 Fluorescent dyes present difficulties for match prediction due to their variable excitation and emission characteristics which depend on a variety of factors. An empirical approach is therefore favored such as that used in the artificial neural network method. Bezerra Hawkyard 2000 described the production of a database with four acid dyes two fluorescent and two non-fluorescent along with the large number of mixture dyeing that were carried out. The data were used to construct a network connecting reflectance values with concentrations in formulations. Their multilayer perceptron network was trained using back propagation algorithm. Network topology was constituted of one input layer three nodes one hidden layer four nodes and one output layer five nodes . the networks input layers were fed with SRF XYZ or L a b values of each sample in order to predict in the output layer the dye concentrations C applied. A linear activation function was used in the input and output layers and the logistic sigmoid function in the hidden layers. All the data were normalized before training and testing and all the networks were trained using the same learning rate and momentum term . The 311 samples produced were divided in two groups a training set 283 samples and a testing set 28 samples . Their results showed that although time consuming the presented approach was viable and accurate Bezerra Hawkyard 2000 . Ameri et al. 2005 used the fundamental color stimulus as the input for a fixed optimized neural network match prediction system. Four sets of data having different origins . different substrate different colorant sets and different dyeing procedures were used to train and test the performance of the network. The input layer was consistent of the measured surface spectral reflectance of the target color centers at 16 wavelengths of 20 nm intervals throughout the visible range of the spectrum