Modeling of Pulping of Sugar Maple Using Advanced Neural Network Learning

This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF...

• Type: Conference
• Year: 2004
• Where: IJIT
• Authors: W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sathasivam
• Keywords: Artificial Intelligence, Conjugate Gradient, IJIT 2004, Neural Networks, Preconditioned