Computational artificial neural networks

Key words drilling polymer composites soft computing artificial neural network. 

The method that was developed builds on computed values of physicochemical effects and uses neural networks for classification. Therefore, for a deeper understanding of this form of reaction classification, later chapters should be consulted on topics such as methods for the calculation of physicochemical effects (Section 7.1) and artificial neural networks (Section 9.4). 

Problems involving routine calculations are solved much faster and more reliably by computers than by humans. Nevertheless, there are tasks in which humans perform better, such as those in which the procedure is not strictly determined and problems which are not strictly algorithmic. One of these tasks is the recognition of patterns such as feces. For several decades people have been trying to develop methods which enable computers to achieve better results in these fields. One approach, artificial neural networks, which model the functionality of the brain, is explained in this section. 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

Since biological systems can reasonably cope with some of these problems, the intuition behind neural nets is that computing systems based on the architecture of the brain can better emulate human cognitive behavior than systems based on symbol manipulation. Unfortunately, the processing characteristics of the brain are as yet incompletely understood. Consequendy, computational systems based on brain architecture are highly simplified models of thek biological analogues. To make this distinction clear, neural nets are often referred to as artificial neural networks. 

Srinivasula S, Jain A (2006) A comparative analysis of training methods for artificial neural network rainfall-runoff models. Appl Soft Comput 6 295-306... 

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

E.P.P.A. Derks, M.L.M. Beckers, W.J. Meissen and L.M.C. Buydens, A parallel cross-validation procedure for artificial neural networks. Computers Chem., 20 (1995) 439-448. 

C. Hoskins and D.M. Himmelblau, Process Control via artificial Neural networks and reinforced learning. Computers Chem. Eng., 16 (1992) 241-251. 

Step 8. Spectra classified using an artificial neural network pattern recognition program. (This program is enabled on a parallel-distributed network of several personal computers [PCs] that facilitates optimization of neural network architecture). 

Systematic optimization of artificial neural network and other advanced computational models for grouping strains and for classifying unknown samples as members of the most appropriate group. 

Connectionist computation which is inspired by multi-cellular systems like artificial neural networks that mimic the way of working of the human brain. 

Numerous books have been written on the topic of artificial neural networks most are written for, or from the point of view of, computer scientists and these are probably less suited to the needs of experimental scientists than those written with a more general audience in mind. 

HT(la) /alpha( 1)-adrenergic receptor affinity by classical Hansch analysis, artificial neural networks, and computational simulation of ligand recognition. Journal of Medicinal Chemistry, 44, 198-207. 

Lopez-Rodriguez, M.L., Morcillo, M.J., Fernandez, E., Rosado, M.L., Pardo, L. and Schaper, K.-f. (2001) Synthesis and structure-activity relationships of a new model of arylpiperazines. 6. Study of the 5-HTiA/ai-adrenergic receptor affinity by classical Hansch analysis, artificial neural networks, and computational simulation of ligand recognition. Journal of Medicinal Chemistry, 44, 198-207. 

Developments in computer technology promoted the use of computationally demanding methods such as artificial neural networks, genetic algorithms, and multiway data analysis. 

Muecia-Soler, M., Perez-Gimenez, F., GAECiA-MARCH, F.J., Salabert-Salvador, M.T., Diaz-Villanueva, W., and Castro-Bleda, M.J. Dmgs and nondmgs an effective discrimination with topological methods and artificial neural networks. /. Chem. Inf. Comput.. Sci. 2003, 43, 1688-1702. 

Artificial neural networks (ANN) are computing tools made up of simple, interconnected processing elements called neurons. The neurons are arranged in layers. The feed-forward network consists of an input layer, one or more hidden layers, and an output layer. ANNs are known to be well suited for assimilating knowledge about complex processes if they are properly subjected to input-output patterns about the process. 

Elkamel, A. (1998) An artificial neural network for predicating and optimizing immiscible flood performance in heterogeneous reservoirs. Computers el Chemical Engineering, 22, 1699. 

Schneider, G. and Wrede, R (1998) Artificial neural networks for computer-based molecular design. Prog. Biophys. Mol. Biol. 70, 175-222. 

J. G. Magallanes, P. Smichowski and J. Marrero, Optimisation and empirical modeling of HG-ICP-AES analytical technique through artificial neural networks, J. Chem. Inf. Comput. Sci., 41(3), 2001, 824-829. 

Z. Roger, R. Weber, Finding an optimal artificial neural network topology in real-life modeling, presented at the ICSC Symposium on Neural Computation, article No. 1, 1403/109, 2000. 

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