Back propagation neural networks

Supeiwised learning strategies are applied in counter-propagation and in back-propagation neural networks (see Sections 9.5.5 and 9.5,7, ... 

Association deals with the extraction of relationships among members of a data set. The methods applied for association range from rather simple ones, e.g., correlation analysis, to more sophisticated methods like counter-propagation or back-propagation neural networks (see Sections 9.5.5 and 9.5.7). 

Breindl et. al. published a model based on semi-empirical quantum mechanical descriptors and back-propagation neural networks [14]. The training data set consisted of 1085 compounds, and 36 descriptors were derived from AMI and PM3 calculations describing electronic and spatial effects. The best results with a standard deviation of 0.41 were obtained with the AMl-based descriptors and a net architecture 16-25-1, corresponding to 451 adjustable parameters and a ratio of 2.17 to the number of input data. For a test data set a standard deviation of 0.53 was reported, which is quite close to the training model. 

Recently, several QSPR solubility prediction models based on a fairly large and diverse data set were generated. Huuskonen developed the models using MLRA and back-propagation neural networks (BPG) on a data set of 1297 diverse compoimds [22]. The compounds were described by 24 atom-type E-state indices and six other topological indices. For the 413 compoimds in the test set, MLRA gave = 0.88 and s = 0.71 and neural network provided... 

Figure 10.1-3. Predicted versus experimental solubility values of 552 compounds in the test set by a back-propagation neural network with 18 topological descriptors.

Feedforward Back-propagation Neural Network %Network structure l 10(tansig) l(purelin)... 

Devillers, J., Domine, D Guillon, C., Karcher, W. Simulating lipophilidty of organic molecules with a back-propagation neural network. J. Pharm. Sci. 1998, 87, 1086-1090. 

The utility of ANNs as a pattern recognition technique in the field of microbeam analysis was demonstrated by Ro and Linton [99]. Back-propagation neural networks were applied to laser microprobe mass spectra (LAMMS) to determine interparticle variations in molecular components. Selforganizing feature maps (Kohonen neural networks) were employed to extract information on molecular distributions within environmental microparticles imaged in cross-section using SIMS. 

Z. Ramadan, P. K. Hopke, M. J. Johnson and K. M. Scow, Application of PLS and back-propagation neural networks for the estimation of soil properties, Chemom. Intell. Lab. Syst., 75(1), 2005, 23-30. 

The configuration of a back-propagation neural network and its use as an internal model controller (IMC). 

To date, two different OCR methods were implemented in CLiDE. The first one used a back-propagation neural network for classification of the characters. The character features used as input to the neural network are determined by template matching (Venczel 1993). The second OCR implementation in CLiDE is based on topological and geometrical feature analysis, and it uses a filtering technique for the classification of characters (Simon 1996). 

O Neill, M. C. (1991). Training back-propagation neural networks to define and detect DNA-binding sites. Nucleic Acids Res 19, 313-8. 

Ville.min D, Cherqaoui D, Mesbah A. Predicting carcinogenicity of polycyclic aromatic hydrocarbons from back-propagation neural network. J Chem Inf Comput Sci 1994 34 1288-93. 

Kahn, L, Sdd, S. and Maran, U. (2007) Modeling the toxicity of chemicals to Tetrahymena pyriformis using heuristic multilinear regression and heuristic back-propagation neural networks. 

Yaffe, D., Cohen, Y, Espinosa, G Arenas, A. and Giralt, E. (2002) Euzzy ARTMAP and back-propagation neural networks based quantitative structure-property relationships (QSPRs) for octanol-water partition coefficient of organic compounds. /. Chem. Inf. Comput. Sci., 42, 162-183. 

SIMCA and related methods Back propagation neural networks Decision trees Genetic algorithms Pattern recognition in data sets A Overview... 

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