Author/Authors :
MAT LAZIM, MOHAMAD AzWANI SHAH Universiti Kebangsaan Malaysia - Fakulti Sains dan Teknologi - Pusat Pengajian Sains Kimia Teknologi Makanan, Malaysia , AHMAD, MUSA Universiti Kebangsaan Malaysia - Fakulti Sains dan Teknologi - Pusat Pengajian Sains Kimia dan Teknologi Makanan, Malaysia , ZAKARIA, ZURIATI Universiti Kebangsaan Malaysia - Fakulti Sains dan Teknologi - Pusat Pengajian Sains Kimia Teknologi Makanan, Malaysia , TAIB, MOHD NASIR Universiti Teknologi MARA - Fakulti Kejuruteraan Elektrik, Malaysia
Abstract :
Artificial neural network (ANN) was used in this study to determine water turbidity by using back propagation algorithm. Three wavelengths which represent reflectance intensity for eight standard samples were used as training input. The finding from the study shows that the trained network with number of epochs of 250, 000 and learning rate of 0.001 gave the lowest sum of squared error (SSE) of 0.04. ANN was able to predict the turbidity of water based on the pattern recognition of the reflectance spectrum. The architecture of optimised ANN used in this study was 3:25: 1. The averageprediction error was 0.02.
