Artificial neural network pattern recognition technique

Artificial Neural Networks (ANNs) attempt to emulate their biological counterparts. McCulloch and Pitts (1943) proposed a simple model of a neuron, and Hebb (1949) described a technique which became known as Hebbian learning. Rosenblatt (1961), devised a single layer of neurons, called a Perceptron, that was used for optical pattern recognition. 

A currently popular approach to classification and pattern-recognition problems involves neural networks. Neural networks are mainly used as (non-)linear approximations to multivariable functions or as classifiers (Ripley, 1993). Principally, the technique is intended to mimic the computational properties of the brain, which is highly parallel in its operation. Artificial neural networks (Figure 3.10) consist of units with some of the properties of real neurons. 

XRF and scattering (EDXRFS) spectroscopy method for direct rapid analysis of trace bioavailable macronutrients (i.e. C, N, Na, Mg, P) in soils. Chemo-metric techniques, namely principal component analysis (PCA), partial least squares (PLS) and artificial neural networks (ANNs), were utilized for pattern recognition based on fluorescence and regions of Compton scatter peaks, and to develop multivariate quantitative calibration models based on Compton scatter peaks, respectively. 

Such definitive classification may be achieved with the aid of multivariate pattern recognition techniques such as hierarchical clustering, linear discriminant analysis (LDA) and artificial neural network analysis. Hierarchical clustering techniques compare sets of data (e.g. individually acquired spectra or spectra acquired by mapping of tissue) and group the data according to some measure of similarity. For mapping data, the application of cluster analysis... 

A more recently introduced technique, at least in the field of chemometrics, is the use of neural networks. The methodology will be described in detail in Chapter 44. In this chapter, we will only give a short and very introductory description to be able to contrast the technique with the others described earlier. A typical artificial neuron is shown in Fig. 33.19. The isolated neuron of this figure performs a two-stage process to transform a set of inputs in a response or output. In a pattern recognition context, these inputs would be the values for the variables (in this example, limited to only 2, X and x- and the response would be a class variable, for instance y = 1 for class K and y = 0 for class L. 

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