Neural network models

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Neural networks model the functionality of the brain. They learn from examples, whereby the weights of the neurons are adapted on the basis of training data. 

Step 6 Building a Back-Propagation (BPC) Neural Network Model... 

Neural networks have been applied to IR spectrum interpreting systems in many variations and applications. Anand [108] introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90%. Robb and Munk [109] used a linear neural network model for interpreting IR spectra for routine analysis purposes, with a similar performance. Ehrentreich et al. [110] used a counterpropagation network based on a strategy of Novic and Zupan [111] to model the correlation of structures and IR spectra. Penchev and co-workers [112] compared three types of spectral features derived from IR peak tables for their ability to be used in automatic classification of IR spectra. 

Nlng Q and T J Sejnowsld 1988. Predicting the Secondary Structure of Globular Proteins Using Neural Network Models. Journal of Molecular Biology 202 865-888. 

Transfer function models are linear in nature, but chemical processes are known to exhibit nonhnear behavior. One could use the same type of optimization objective as given in Eq. (8-26) to determine parameters in nonlinear first-principle models, such as Eq. (8-3) presented earlier. Also, nonhnear empirical models, such as neural network models, have recently been proposed for process applications. The key to the use of these nonlinear empirical models is naving high-quality process data, which allows the important nonhnearities to be identified. 

The first is the relational model. Examples are hnear (i.e., models linear in the parameters and neural network models). The model output is related to the input and specifications using empirical relations bearing no physical relation to the actual chemical process. These models give trends in the output as the input and specifications change. Actual unit performance and model predictions may not be very close. Relational models are usebil as interpolating tools. 

Intended Use The intended use of the model sets the sophistication required. Relational models are adequate for control within narrow bands of setpoints. Physical models are reqiiired for fault detection and design. Even when relational models are used, they are frequently developed bv repeated simulations using physical models. Further, artificial neural-network models used in analysis of plant performance including gross error detection are in their infancy. Readers are referred to the work of Himmelblau for these developments. [For example, see Terry and Himmelblau (1993) cited in the reference list.] Process simulators are in wide use and readily available to engineers. Consequently, the emphasis of this section is to develop a pre-liminaiy physical model representing the unit. 

N Qian, TJ Sejnowski. Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202 865-884, 1988. 

Fig. 10.26 Training and trained data fora neural network model of a Ship s Hull.

Internal Model Control was diseussed in relation to robust eontrol in seetion 9.6.3 and Figure 9.19. The IMC strueture is also applieable to neural network eontrol. The plant model GmC) in Figure 9.19 is replaeed by a neural network model and the eontroller C(.v) by an inverse neural network plant model as shown in Figure 10.30. 

In Figure 10.30 the predietive neural network model traeks the ehanging dynamies of the plant. Following a suitable time delay, em(kT) is passed to the performanee index table. If this indieates poor performanee as a result of ehanged plant dynamies, the rulebase is adjusted aeeordingly. Riehter (2000) demonstrated that this teehnique eould improve and stabilize a SOFLC when applied to the autopilot of a small motorized surfaee vessel. 

The second main category of neural networks is the feedforward type. In this type of network, the signals go in only one direction there are no loops in the system as shown in Fig. 3. The earliest neural network models were linear feed forward. In 1972, two simultaneous articles independently proposed the same model for an associative memory, the linear associator. J. A. Anderson [17], neurophysiologist, and Teuvo Kohonen [18], an electrical engineer, were unaware of each other s work. Today, the most commonly used neural networks are nonlinear feed-forward models. 

A very simple 2-4-1 neural network architecture with two input nodes, one hidden layer with four nodes, and one output node was used in each case. The two input variables were the number of methylene groups and the temperature. Although neural networks have the ability to learn all the differences, differentials, and other calculated inputs directly from the raw data, the training time for the network can be reduced considerably if these values are provided as inputs. The predicted variable was the density of the ester. The neural network model was trained for discrete numbers of methylene groups over the entire temperature range of 300-500 K. The... 

A list of the systems investigated in this work is presented in Tables 8-10. These systems represent 4 nonpolar binaries, 8 nonpolar/polar binaries, and 9 polar binaries. These binary systems were recognized by Heil and Prausnitz [55] as those which had been well studied for a wide range of concentrations. With well-documented behavior they represent a severe test for any proposed model. The experimental data used in this work have been obtained from the work of Alessandro [53]. The experimental data were arbitrarily divided into two data sets one for use in training the proposed neural network model and the remainder for validating the trained network. 

To evaluate the reliability of the proposed neural network model, all the binaries were trained in each of the... 

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. 

The proposed neural network model with the nonlinear optimization routine is similar to many nonlinear... 

R. Keshavaraj, R. W. Tock, and D. Haycook, Feedforward Neural Network Modeling of Biaxial Deformation of Airbag Fabrics ANTEC 95 Proceedings, SPE Technical Papers, Modeling of Polymer Properties and Processes, Boston (May 1995). 

Chen et al. [24] provide a good review of Al techniques used for modeling environmental systems. Pongracz et al. [25] presents the application of a fuzzy-rule based modeling technique to predict regional drought. Artificial neural networks model have been applied for mountainous water-resources management in Cyprus [26] and to forecast raw-water quality parameters for the North Saskatchewan River [27]. 

Iliadis LS, Maris E (2007) An artificial neural network model for mountainous water-resources management the case of Cyprus mountainous watersheds. Environ Modell Softw 22 1066-1072... 

Zhang Q, Stanley SJ (1997) Eorecasting raw-water quality parameters for the North Saskatchewan River by neural network modelling. Water Res 31 2340-2350... 

Hoskins, J. C., and Himmelblau, D. M., Artificial neural network models of knowledge representation in chemical engineering. Comput. Chem. Eng. 12, 881 (1988). 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

High quality of property predictions by Molconn-Z and artificial neural network modeling. Ahstr. Papers Am. Chem. Soc. 2000, 220, U288. 

I. V. Neural network modeling for estimation of partition coeffident based on atom-type electrotopological state indices. 

R. Tibshirani, A comparison of some error estimates for neural network models. Neural Computation, 8(1995) 152-163. 

M.E. Munk, M.S. Madison and E.W. Robb, Neural network models for infrared spectrum interpretation. Microchim. Acta, 2 (1991) 505-524. 

Harrington, P. B. Voorhees, K. J. Franco, B. Hendricker, A. D. Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra. J. Am. Soc. Mass Spectrom. 2002,13,10-21. 

Hemmateenejad, B., et al., Accurate prediction of the bloodbrain partitioning of a large set of solutes using ab intio calculations and genetic neural network modeling, /. Comp. Chem., 27,1125,2006. 

Huuskonen, J., Salo, M., Taskinen, J., Aqueous solubility prediction of drugs based on molecular topology and neural network modeling, J. 

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