Artificial neural network investigation

Albahri, TA. and George, R.S. (2003) Artificial neural network investigation of the structural group contribution method for predicting pure components auto ignition temperature. Ind. Eng. Chem. Res., 42, 5708-5714. 

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]. 

A helpful starting point for further investigation is Learning Classifier Systems From Foundations to Applications.1 The literature in classifier systems is far thinner than that in genetic algorithms, artificial neural networks, and other methods discussed in this book. A productive way to uncover more... 

A novel approach to reduce the experimental effort associated with constructing pseudoternary phase diagrams is by using expert systems to predict the phase behavior of multicomponent ME-forming systems. Artificial neural networks have been investigated and were shown to be promising in phase behavior studies [17,35,36] as well as in the process of ingredient selection [37]. 

Pedersen, A. G. Engelbrecht, J. (1995). Investigations of Escherichia coli promoter sequences with artificial neural networks new signals discovered upstream of the transcriptional startpoint. Ismb 3,292-9. 

Stephan C, Jung K, Cammann H, et al. An artificial neural network considerably improves the diagnostic power of percent free prostate-specific antigen in prostate cancer diagnosis results of a 5-year investigation. Int J Cancer 2002 99 466-73. 

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. 

Sozen, A. Ozalp, M. Arcaklyoglu, E. Investigation of thermodynamic properties of refrigerant/ absorbent couples using artificial neural networks. Chem. Eng. Process. 2004, 43, 1253-1264. 

To establish a correlation between the concentrations of different kinds of nucleosides in a complex metabolic system and normal or abnormal states of human bodies, computer-aided pattern recognition methods are required (15, 16). Different kinds of pattern recognition methods based on multivariate data analysis such as principal component analysis (PCA) (8), partial least squares (16), stepwise discriminant analysis, and canonical discriminant analysis (10, 11) have been reported. Linear discriminant analysis (17, 18) and cluster analysis were also investigated (19,20). Artificial neural network (ANN) is a branch of chemometrics that resolves regression or classification problems. The applications of ANN in separation science and chemistry have been reported widely (21-23). For pattern recognition analysis in clinical study, ANN was also proven to be a promising method (8). 

Artificial neural networks (ANNs) are programs designed to simulate the way a simple biological nervous system is believed to operate. They are based on simulated nerve cells or neurons that are joined together in a variety of ways to form networks. These networks have the capacity to learn, memorize and create relationships amongst data [307-313] or chemical characteristics [314-319]. There are many different types of ANNs that can be used in environmental forensic investigations, but some are more popular than others. The most widely used ANN is known as the Back Propagation ANN. This type of ANN is excellent at prediction and classification tasks. Another is the Kohonen or Self... 

Lee et al. [36] developed an artificial neural network model for use to design and analysis PEM fuel cell power systems. The artificial neural network model can simulate the experimental data for different operating conditions and hence can be used to investigate the influence of process variables. 

Patnaik reported fed-batch optimization of PHB synthesis through mechanistic, cybernetic, and neural approaches. Enhancement of PHB productivity was investigated by applying two artificial neural networks to a bioreactor with finite dispersion and noise in feed streams. One network filtered the noise and other controlled the filtered feed rates of carbon and nitrogen sources. The study revealed that neural optimization doubled the maximum PHB concentration in fed-batch fermentation with R. eutropha by optimizing the time dependent feed rates. 

It is well known that electrical conductivity of liquid solutions depends on the concentration of ions and their activity. The aqueous fluids in pipelines usually are electrolyte solutions and the conductivity is proportional to the salt concentration. The activity of the ions is related to temperature, and impurity like nonconductive chemical additives. Measurements of electrical conductivity could directly reflect the concentrations of chemicals such as salts, THIs (alcohol) and KHIs (polymers). (Clay and Medwin, 1977) [6] presented a simple correlation in which the sound velocity in sea water was described as a function of sahnity and temperature. Acoustic velocity has been successfully applied to investigate a variety of solutions and binary gas mixtures (Jerie, et al., 2004 Vibhu, et al., 2004 Goodenough, et al., 2005 Vyas, et al., 2006) [11] [25] [9] [26]. As a result, electrical conductivity and acoustic velocity were chosen as two parameters to simultaneously determine both salt and inhibitor concentrations. Artificial neural network (ANN) provides a numerical tool for such applications in which multi-parameter correlations are needed but the interaction and the relations between the parameters are not well known (Sundgren, et al., 1991 Broten and Wood, 1993) [21] [4]. Therefore, ANN correlations were developed to determine salt and inhibitor concentrations using the measured electrical conductivity, acoustic velocity, and temperature. 

The final detected polyps are obtained by application of a statistical classifier based on the image features to the differentiation of polyps from false positives. Investigators use parametric classifiers such as quadratic discriminant analysis (Yoshida and Nappi 2001), non-parametric classifiers such as artificial neural networks (Jerebko et al. 2003b Kiss et al. 2002 Nappi et al. 2004b), a committee of neural networks (Jerebko et al. 2003a), and a support vector machine (Gokturk et al. 2001). In principle, any combination of features and a classifier that provides a high classification performance should be sufficient for the differentiation task. 

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