Back propagation network pattern recognition

Some of the pioneering studies published by several reputed authors in the chemometrics field [55] employed Kohonen neural networks to diagnose calibration problems related to the use of AAS spectral lines. As they focused on classifying potential calibration lines, they used Kohonen neural networks to perform a sort of pattern recognition. Often Kohonen nets (which were outlined briefly in Section 5.4.1) are best suited to perform classification tasks, whereas error back-propagation feed-forwards (BPNs) are preferred for calibration purposes [56]. 

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. 

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

H. Schulz, M. Derrick, and D. Stulik, Ana/. Chim. Acta, 316,145 (1995). Simple Encoding of Infrared Spectra for Pattern Recognition. 2. Neural Network Approach Using Back-Propagation and Associative Hopfield Memory. 

Within the field of chemistry, various applications have already been published. Jansson " and Zupan and Gasteiger published an overview of an MLP (multilayer perceptron), that is trained by back-propagation of errors, and other types of neural networks. However, the basic source in this field continues to be the well-known book of Zupan and Gasteiger." In analytical chemistry, neural networks have been applied to pattern recognition, modeling, and prediction, for example, in multicomponent analysis or process control, to classification, clustering and pattern association. 

Fig. 1. Pattern recognition methods. ANN, artificial neural networks BP ANN, back-propagation ANN CA, cluster analysis CART, classification and regression trees (recursive partitioning) CCA, canonical correlation analysis CVA, canonical variate analysis kNN, -nearest neighbor methods LDA, linear discriminant analysis PCA, principal component analysis PLS DA, partial least squares regression discriminant analysis SIMCA, soft independent modeling of class analogy SOM, self-organizing maps.

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