Artificial neural networks optimization

Sanders, J.C., Breadmore, M.C., Kwok, Y.C., Horsman, K.M., Landers, J.P., Hydroxypropyl cellulose as an adsorptive coating sieving matrix for DNA separations Artificial neural network optimization for microchip analysis. Anal. Chem. 2003, 75, 986-994. 

Chemoinformati.cs is involved in the drug discovery process in both the lead finding and lead optimization steps. Artificial neural networks can play a decisive role of various stages in this process cf. Section 10.4.7.1). 

Concomitantly with the increase in hardware capabilities, better software techniques will have to be developed. It will pay us to continue to learn how nature tackles problems. Artificial neural networks are a far cry away from the capabilities of the human brain. There is a lot of room left from the information processing of the human brain in order to develop more powerful artificial neural networks. Nature has developed over millions of years efficient optimization methods for adapting to changes in the environment. The development of evolutionary and genetic algorithms will continue. 

Takayama K, Fujikawa M, Nagai T. Artificial neural networks as a novel method to optimize pharmaceutical formulations. Pharm Res 1999 16 1-6. 

Takahara J, Takayama K, Nagai T. Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations. 

Zupancic Bozic D, Vrecar F, Kozjek F. Optimization of diclofenac sodium dissolution from sustained release formulations using an artificial neural network. Eur... 

Ibric S, Jovanovic M, Djuric A, Parojcic J, Petrovic SD, Solomun L, Stupor B. Artificial neural networks in the modelling and optimization of aspirin extended release tablets with Eudragit LlOO as matrix substance. Pharm Sci Tech 2003 4 62-70. 

Wu T, Pao W, Chen J, Shang R. Formulation optimization technique based on artificial neural network in salbutamol sulfate osmotic pump tablets. Drug Dev Ind Pharm 2000 26 211-15. 

Takayama K, Takahara J, Fujikawa M, Ichikawa H, Nagai T. Formula optimization based on artificial neural networks in transdermal drug delivery. J Controlled Release 1999 62 161-70. 

Wu P-C, Obata Y, Fijukawa M, Li CJ, Higashiyama K, Takayama K. Simultaneous optimization based on artificial neural networks in ketoprofen hydrogel formula containing o-ethyl-3-burylcyclohexanol as a percutaneous absorption enhancer. J Pharm Sci 2001 90 1004-14. 

Kandimalla KK, Kanikkannon N, Singh M. Optimization of a vehicle mixture for the transdermal delivery of melatonin using artificial neural networks and response surface method. / Controlled Release 1999 61 71-82. 

Wu et al. [46] used the approach of an artificial neural network and applied it to drug release from osmotic pump tablets based on several coating parameters. Gabrielsson et al. [47] applied several different multivariate methods for both screening and optimization applied to the general topic of tablet formulation they included principal component analysis and... 

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. 

In analytical chemistry, Artificial Neural Networks (ANN) are mostly used for calibration, see Sect. 6.5, and classification problems. On the other hand, feedback networks are usefully to apply for optimization problems, especially nets ofHoPFiELD type (Hopfield [1982] Lee and Sheu [1990]). 

There has been a notable change in the way that the GA has been used as it has become more popular in science. Some of the earliest applications of the GA in chemistry (for example, the work of Hugh Cartwright and Robert Long, and Andrew Tuson and Hugh Cartwright on chemical flowshops) used the GA as the sole optimization tool many more recent applications combine the GA with a second technique, such as an artificial neural network. 

Compared with the artificial neural network (ANN) approach used in previous work to predict CN12 the linear regression model by QSAR is as good or better and easier to implement. The predicted CN values, some of which are tabulated in Table 1, will be employed below to evaluate the different catalytic strategies to optimize the fuel. 

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

In many modeling techniques, the number of parameters is modified many times looking for a setting that provides the maximum predictive ability for the model. Techniques for variable selection and methods based on artificial neural networks perform an optimization, that is, they search for conditions able to provide the maximum predictive ability possible for a given sample subset. 

Z. Roger, Selection of the quasi-optimal inputs in chemometric modelling by artificial neural networks analysis, Anal. Chim. Acta, 490(1-2), 2003, 31-40. 

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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