Neural backpropagation

In many cases, structure elucidation with artificial neural networks is limited to backpropagation networks [113] and, is therefore performed in a supervised man-... 

T. J. McAvoy and co-workers, "Interpreting Biosensor Data via Backpropagation," in Proceedings of the InternationalJoint Conf. on Neural Networks, Washington, D.C., 1989. 

Numeric-to-numeric transformations are used as empirical mathematical models where the adaptive characteristics of neural networks learn to map between numeric sets of input-output data. In these modehng apphcations, neural networks are used as an alternative to traditional data regression schemes based on regression of plant data. Backpropagation networks have been widely used for this purpose. 

Garrido L, Gomez S, Roca J. Improved multidimensional scaling analysis using neural networks with distance-error backpropagation. Neural Comput 1999 11 595-600. 

Hernandez, E., and Arkun, Y., A study of the control relevant properties of backpropagation neural net models of nonlinear dynamical systems. Comput. Chem. Eng. 16, 227 (1992). 

B.J. Wythoff, Backpropagation neural networks — a tutorial. Chemom. Intell. Lab. Syst., 18 (1993) 115-155. 

B. Walczak, Neural networks with robust backpropagation learning algorithm. Anal. Chim. Acta, 322 (1996) 21-30. 

By design, ANNs are inherently flexible (can map nonlinear relationships). They produce models well suited for classification of diverse bacteria. Examples of pattern analysis using ANNs for biochemical analysis by PyMS can be traced back to the early 1990s.4fM7 In order to better demonstrate the power of neural network analysis for pathogen ID, a brief background of artificial neural network principles is provided. In particular, backpropagation artificial neural network (backprop ANN) principles are discussed, since that is the most commonly used type of ANN. 

Wythoff BJ (1993) Backpropagation neural networks. A tutorial. Chemom Intell Lab Syst 18 115... 

The lack of a recipe for adjusting the weights of connections into hidden nodes brought research in neural networks to a virtual standstill until the publication by Rumelhart, Hinton, and Williams2 of a technique now known as backpropagation (BP). This offered a way out of the difficulty. 

Riedmiller, M. Braun, H. (1993). A (Greet adaptive method for faster backpropagation learning the Rprop algorithm. In Proceedings of the IEEE International Conference on Neural Networks (ICNN 93). (ed. Ruspini H.), pp. 586-91. 

Backpropagation Neural Networks NN1 (Sequence-Structure) --------- NN2 (Structure-Structure)... 

Villemin, D., Cherqaoui, D. and Mesbah, A. (1994). Predicting Carcinogenicity of Polycyclic Aromatic Hydrocarbons from Backpropagation Neural Network. J.Chem.lnf.Comput.Sci., 34, 1288-1293. 

For kriging models, the structure is defined by the set of independent variables selected - including quadratic terms - and the selection of the correlation model. The parameter estimation is performed by a maximum likelihood procedure. For neural nets, the activation function to be used is defined a priori. The structure is completed by the selection of the number of neurons in the hidden layer. A backpropagation procedure has been used for training. 

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