Neural network models, for

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. 

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... 

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

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. 

Chen, C.R., Ramaswamy, H.S., and Alii, 1.2001. Prediction of quality changes during osmoconvective drying of blueberries using neural network models for process optimisation. Dry. Technol. 19, 507-523. 

The neural network model for the two binary systems viz. tert-butanol+2-ethyl-l-hexanol and n-butanol+2-ethyl-l-hexanol is based on the experimental data reported by Ghanadzadeh et al. [23], The summary of the data is shown in tables 1 and 2. All neural networks take numeric input and produce numeric output. The transformation function of a neuron is typically chosen so that it can accept input in any range, and produce output in a strictly limited range. Although the input can be in any range, there is a saturation effect so that the unit is only sensitive to inputs within a fairly limited range. Numeric values have to be scaled into a range that is appropriate for the network. 

E. Richards, C. Bessant and S. Saini, Optimisation of a neural network model for calibration of voltametric data, Chemom. Intell. Lab. Syst., 61(1-2), 2002, 35 49. 

Pasini A. Lore M. and Ameli F. (2006). Neural network modelling for the analysis of forcings/ temperatures relationships at different scales in the climate system. Ecological Modelling, 191(1), 58-67. 

Chen VCP, Rollins DK (2000), Issues regarding artificial neural network modeling for reactors and fermenters, Bioprocess. Eng. 22 85-93. 

Lohmann, R., Schneider, G., Behrens, D. Wrede, P. (1994). A neural network model for the prediction of membrane-spanning amino acid sequences. Protein Sci 3,1597-601. 

Do Nascimento CAO, Oliveros E, Braun AM (1994) Neural Network Modelling for Photochemical Processes, Chem. Eng. Proc. 33 319-324. 

Huuskonen, J. Salo, M. Taskinen, J. Neural network modeling for estimation of the aqueous solubility of structurally related drugs, / Pharm Sci. 1997, 86 (4), 450-454. 

Neural network models for the prediction of the amount of desired product CcftfVitf)... 

Kaiser KLE, Niculescu SP, Schultz TW. Probabilistic neural network modeling for the toxicity of chemicals to Tetrahymena pyriformis with molecular fragment descriptors. SAR QSAR Environ Res 2002 13 57-67. 

Munk, M.E., Madison, M.S., and Robb, E.W., Neural Network Models for Infrared Spectrum Interpretation, Mikrochim. Acta, [Wien] 2, 505, 1991. 

Biinz, A.P., Braun, B. and Janowsky, R. (1998) Application of quantitative structure-performance relationship and neural network models for the prediction of physical properties from molecular structure. Ind. Eng. Chem. Res., 37, 3044—3051. 

Huuskonen, J.J., Livingstone, D.J. and Tetko, I.V. (2000) Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices. /. Chem, Inf, Comput, Sci, 40, 947-955. 

We will now review the neural network model for memory and study how the knowledge of orthogonalization methods can help us in understanding certain cognitive functions. 

Molz, R. F, Engel, P. M., Moraes, F. G., Torres, L., and Robert, M. "A Fast Prototyping Neural Network Model for Image Classification." In Proceedings of the XV Conference on Design of Circuit and Integrated Systems (DCIS), 1 (2000) 836-41. 

Mohd. Ekhwan Toriman and Hafizan Juahir, 2003. Artificial Neural Network Modelling For Langat River Discharge Implication For River Restoration. Pertandingan Minggu Penyelidikan dan Inovasi UKM, Pusat Pengurusan Penyelidikan, 3-5 Julai. 

Carpenter, G. 1989. Neural network models for pattern recognition and associative memory. Neural Networks, 2 243-258. 

Kawato, M., Furukawa, K., and Suzuki, R. 1987. A hierarchical neural-network model for control and learning of voluntary movement. Biol. Cybem., 57 169-185. 

Kawato, M., Computational schemes and neural network models for formation and control of multi-joint arm trajectory. In Miller, W.T, Sutton, R.T., and Werbos, P.J. (Eds.), Neural Network for Control. MIT Press, Cambridge, MA, 1990. 

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