1. Introduction to neural networks.
1.2 Artificial neural networks from an engineering perspective.
1.3 Brief history of neural networks.
1.4 Structures of neural networks.
1.5 Training rules.
1.6 Neural, network models.
1.7 Neural networks and statistical models.
1.8 Applications in the process industries.
2. Training of neural networks.
2.1 Gradient descent methods.
2.2 Conjugate gradients.
2.3 Newton's method and quasi-Newton method.
2.4 Levenberg-Marquardt algorithm.
2.5 Stochastic methods.
2.6 Regularization and pruning of neural network model.
2.7 Pruning algorithms for neural networks.
2.8 Constructive algorithms for neural network.
3. Latent variable methods.
3.1 Basics of latent structure analysis.
3.2 Principal component analysis.
3.3 Nonlinear approaches to latent variable extraction.
3.4 Principal component analysis with neural networks.
3.5 Example 2: feature extraction from digitised images of industrial flotation froths with auto associative neural networks.
3.6 Alternative approaches to nonlinear principal component analysis.
3.7 Example 1: low-dimensional reconstruction of data with non linear principal component methods.
3.8 Partial least squares (PLS) models.
3.9 Multivariate statistical process control.
4. Regression models.
4.1 Theoretical background to model development.
4.2 Regression and correlation.
4.4 Outliers and influential observations.
4.5 Robust regression models.
4.6 Dummy variable regression.
4.7 Ridge regression. <