Artificial neural network cases

In many cases, structure elucidation with artificial neural networks is limited to backpropagation networks [113] and, is therefore performed in a supervised man-... 

Recently, a new approach called artificial neural networks (ANNs) is assisting engineers and scientists in their assessment of fuzzy information, Polymer scientists often face a situation where the rules governing the particular system are unknown or difficult to use. It also frequently becomes an arduous task to develop functional forms/empirical equations to describe a phenomena. Most of these complexities can be overcome with an ANN approach because of its ability to build an internal model based solely on the exposure in a training environment. Fault tolerance of ANNs has been found to be very advantageous in physical property predictions of polymers. This chapter presents a few such cases where the authors have successfully implemented an ANN-based approach for purpose of empirical modeling. These are not exhaustive by any means. 

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... 

In an early application of in silico approaches to predict human VD, Ritschel and coworkers described an approach using artificial neural networks (ANN), in this case for VDp [34]. However, this was not a truly in silico-only approach as the ANN that yielded accurate predictions of human VD required animal pharmacokinetic data as input parameters, along with in vitro data (protein binding and logP). 

Chen et al. (2008) employed a commercial electronic tongue, based on an array of seven sensors, to classify 80 green tea samples on the basis of their taste grade, which is usually assessed by a panel test. PCA was employed as an explorative tool, while fc-NN and a back propagation artificial neural network (BP-ANN) were used for supervised classification. Both the techniques provide excellent results, achieving 100% prediction ability on a test set composed of 40 samples (one-half of the total number). In cases like this, when a simple technique, such as fc-NN, is able to supply excellent outcomes, the utilization of a complex technique, like BP-ANN, does not appear justified from a practical point of view. 

In the early days of catalyst screening, speed was the only important matter. This meant collecting as much information as possible on a certain catalyst under defined process parameters. This approach produces a large number of non-interrelated single data points with a low degree of information. As soon as correlations between these data can be found, the information density increases. This is the case if reaction kinetics are derived from single data points or if a supervised artificial neural network has learned to predict relations between data points. 

Thus, multilinear models were introduced, and then a wide series of tools, such as nonlinear models, including artificial neural networks, fuzzy logic, Bayesian models, and expert systems. A number of reviews deal with the different techniques [4-6]. Mathematical techniques have also been used to keep into account the high number (up to several thousands) of chemical descriptors and fragments that can be used for modeling purposes, with the problem of increase in noise and lack of statistical robustness. Also in this case, linear and nonlinear methods have been used, such as principal component analysis (PCA) and genetic algorithms (GA) [6]. 

Another important feature of mathematical modeling techniques is the nature of the response data that they are capable of handling. Some methods are designed to work with data that are measured on a nominal or ordinal scale this means the results are divided into two or more classes that may bear some relation to one another. Male and female, dead and alive, and aromatic and nonaromatic, are all classifications (dichotomous in this case) based on a nominal scale. Toxic, slightly toxic, and non-toxic are classifications based on an ordinal scale since they can be written as toxic > slightly toxic > non-toxic. The rest of this section is divided into three parts methods that deal with classified responses, methods that handle continuous data, and artificial neural networks that can be used for both. 

In most cases, the MFTA models are built using the Partial Least Squares Regression (PLSR) technique that is suitable for the stable modeling based on the excessive and/or correlated descriptors (under-defined data sets). However, the MFTA approach is not limited to the PLSR models and can successfully employ other statistical learning techniques such as the Artificial Neural Networks (ANN) supporting the detection of the nonlinear structure-activity relationships. ... 

Zolotariou, R. Anwar, J. Modelling properties of powders using artificial neural networks and regression the case of limited data. J. Pharm. Pharmacol. 1998, 50 (Suppl)190. 

Lately, there has been a great deal of interest in the use of artificial neural networks in many fields, including that of prediction and expert systems, and they are of interest here for the description of response surfaces that have a non-linear relation to the factor vari-ables. In such cases, the response surface may well fit the data better than that calculated from the model estimated by least-squares regression. " ... 

M-CASE/BAIA (see text). BP-ANN = three-layer feedforward artificial neural network trained by the backpropagation algorithm, PAAN = probabilistic artificial neural network, CPANN = counterpropagation artificial neural network. 

The major limitation of the simple perceptron model is that it fails drastically on linearly inseparable pattern recognition problems. For a solution to these cases we must investigate the properties and abilities of multilayer perceptrons and artificial neural networks. 

Artificial neural networks (ANNs) represent, as opposed to PLS and MLR, a nonlinear statistical analysis technique [86]. The most commonly used N N is of the feed-forward back-propagation type (Figure 14.2). As is the case of both PLS and MLR, there are a few aspects of NN to be considered when using this type of analysis technique ... 

Dielectric barrier discharge reactor for conversion of methane and CO2 into synthesis gas and C2+ hydrocarbons Three cases (a) maximization of metiiane conversion and C2+ selectivity, (b) maximization of methane conversion and C2+ yield, and (c) maximization of methane conversion and H2 selectivity. Weighted sum of squared objective functions method along with GA An artificial neural network model of the process was developed based on experimental data, and then used for optimization. Istadi and Amin (2006)... 

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