Neural counterpropagation

Counterpropagation neural networks (CFG NN) were then used to establish relationships between protons and their H NMR chemical shifts. A detailed description of this method is given in the Tools Section 10,2.4.2,... 

A combination of physicochemical, topological, and geometric information is used to encode the environment of a proton, The geometric information is based on (local) proton radial distribution function (RDF) descriptors and characterizes the 3D environment of the proton. Counterpropagation neural networks established the relationship between protons and their h NMR chemical shifts (for details of neural networks, see Section 9,5). Four different types of protons were... 

Figure 10.2-9. Application of a counterpropagation neural network as a look-up table for IR spectra sinnulation, The winning neuron which contains the RDF code in the upper layer of the network points to the simulated IR spectrum in the lower layer.

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. 

Several methods have been developed for establishing correlations between IR vibrational bands and substructure fragments. Counterpropagation neural networks were used to make predictions of the full spectra from RDF codes of the molecules. 

Several nonlinear QSAR methods have been proposed in recent years. Most of these methods are based on either ANN or machine learning techniques. Both back-propagation (BP-ANN) and counterpropagation (CP-ANN) neural networks [33] were used in these studies. Because optimization of many parameters is involved in these techniques, the speed of the analysis is relatively slow. More recently, Hirst reported a simple and fast nonlinear QSAR method in which the activity surface was generated from the activities of training set compounds based on some predefined mathematical functions [34]. 

Another type of ANNs widely employed is represented by the Kohonen self organizing maps (SOMs), used for unsupervised exploratory analysis, and by the counterpropagation (CP) neural networks, used for nonlinear regression and classification (Marini, 2009). Also, these tools require a considerable number of objects to build reliable models and a severe validation. 

Pompe, M., Razinger, M., Novic, M. and Veber, M. (1997). Modelling of Gas Chromatographic Retention Indices Using Counterpropagation Neural Networks. Anal.Chim.Acta, 348, 215-221. 

M-CASE/BAIA (see text). BP-ANN = three-layer feedforward artificial neural network trained by the backpropagation algorithm, PAAN = probabilistic artificial neural network, CPANN = counterpropagation artificial neural network. 

Artificial neural networks (feed-forward neural networks, self-organizing neural networks, counterpropagation neural networks, Bayesian neural networks)... 

Counterpropagation (CPG) Neural Networks are a type of ANN consisting of multiple layers (i.e., input, output, map) in which the hidden layer is a Kohonen neural network. This model eliminates the need for back-propagation, thereby reducing training time. 

Novic, M. and Zupan, J., Investigation of Infrared Spectra-Structure Correlation Using Kohonen and Counterpropagation Neural Network, J. Chem. Inf. Comput. Sci., 35, 454, 1995. 

Arakawa, M., Hasegawa, K. and Funatsu, K. (2006) QSAR study of anti-HIV HEPTanalogues based on multi-objective genetic programming and counterpropagation neural network. Chemom. Intell. Lab. Syst., 83, 91-98. 

Zupan, J., Novic, M. and Ruisanchez, I. (1997) Kohonen and counterpropagation artificial neural networks in analytical chemistry. Chemom. Intell. Lab. Syst., 38, 1—23. 

It is clear that artificial neural networks have become a viable tool for chemists. It is not clear, however, that they are consistently superior to statistical methods. It does seem obvious that with a few possible exceptions (counterpropagation and Kohonen networks), it is more difficult to derive physical meaning from network analysis than it is from statistical methods. Even relatively simple and straightforward questions (e.g.. Which variables are most... 

R. Hecht-Nielsen, Neural Networks, 1, 131 (1988). Applicadons of Counterpropagation Networks. 

K. L. Peterson, Phys. Rev. A, 44,126 (1991). Classification of Cmll and Pul Energy Levels Using Counterpropagation Neural Networks. 

Hecht-Nielsen, R. 1988. Applications of counterpropagation networks. Neural Networks 1 131-139. 

Hecht-Nielsen, R. (1987) Counterpropagation networks. Proceedings of the 1987 IEEE First International Conference on Neural Networks, vol. II, pp. 19-32. 

ANN = artificial neural network CPG = counterpropagation 3D-MoRSE = 3D molecular representation of structures based on electron diffraction FREL = fragment reduced to an environment that is limited. 

ANN = artificial neural networks CP ANN = counterpropagation artificial neural networks EBP ANN = error-back-propagation artificial neural network XOR = exclusive-or logical operation. 

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