Neural network-based prediction models for physical properties of oil palm medium density fiberboard / Faridah Sh. Ismail

Medium Density Fiberboard (MDF) is an alternative to solid wood used in furniture industries. As an engineered wood, MDF needs to establish the strength level to guarantee its quality. The test procedures for mechanical and physical properties of MDF should conform to a specified standard, prior to releasing processed fiberboards for manufacturing. These tests are costly for they involve a high amount of resources, especially to research institutions. The primary aim of this research is to reduce testing time of three lengthy procedures; namely, 24-hour thickness swelling, 24-hour water absorption and 48-hour moisture content. An intelligent predictive model will replace the lengthy procedures by predicting the properties using known fiberboard characteristics. Back-propagation algorithm is a training method widely used in a multilayer perceptron Neural Network model. It optimizes random values for network weights and biases. However, the result normally faces local optima problems. This situation can be solved by embedding Genetic Algorithm (GA) in the network to replace back-propagation method…