Neural network proposed approach

An artificial neural network based approach for modeling physical properties of nine different siloxanes as a function of temperature and molecular configuration will be presented. Specifically, the specific volumes and the viscosities of nine siloxanes were investigated. The predictions of the proposed model agreed well with the experimental data [41]. 

In this approach, connectivity indices were used as the principle descriptor of the topology of the repeat unit of a polymer. The connectivity indices of various polymers were first correlated directly with the experimental data for six different physical properties. The six properties were Van der Waals volume (Vw), molar volume (V), heat capacity (Cp), solubility parameter (5), glass transition temperature Tfj, and cohesive energies ( coh) for the 45 different polymers. Available data were used to establish the dependence of these properties on the topological indices. All the experimental data for these properties were trained simultaneously in the proposed neural network model in order to develop an overall cause-effect relationship for all six properties. 

In recent years there has been much activity to devise methods for multivariate calibration that take non-linearities into account. Artificial neural networks (Chapter 44) are well suited for modelling non-linear behaviour and they have been applied with success in the field of multivariate calibration [47,48]. A drawback of neural net models is that interpretation and visualization of the model is difficult. Several non-linear variants of PCR and PLS regression have been proposed. Conceptually, the simplest approach towards introducing non-linearity in the regression model is to augment the set of predictor variables (jt, X2, ) with their respective squared terms (xf,. ..) and, optionally, their possible cross-product... 

A model based on the same set of quantum-chemical descriptors was also proposed for aqueous solubility by a neural network approach [Bodor et al., 1991 Bodor et al., 1992c Bodor et al., 1994]. 

It has been proposed that the best tool to model non-linear environmental relationship is ANN (Zhang and Stanley, 1997 Jain and Indurthy, 2003). Research have been undertaken at Imperial College, London which attempts to investigate the capability of ANN approach in modelling spatial and temporal variations in river water quality (Clarici, 1995). ANNs were used as a predictive model to predict cyanobacteria Anabaena spp. in the River Murray, South Australia (Maier et al., 1998). DeSilets et al. (1992), have also used ANN to predict salinity. Ha and Stenstrom (2003), proposed a neural network approach to examine the relationship between storm water quality and various types of land use. 

As mentioned above, Cho and Wysk (1993) utilized the multilayer perceptron to take the place of the knowledge-based system in selecting candidate scheduling rules. In their proposed framework, the neural network will output a goodness index for each rule based on the system attributes and a performance measure. Sim et al. (1994) used an expert neural network for the job shop scheduling problem. In their approach, an expert system will activate one of 16 subnetworks based on whether the attribute corresponding to the node (scheduling rules, arrival rate factor, and criterion) is applicable to the job under consideration. Then the job with the smallest output value wiU be selected to process. 

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