Pattern recognition artificial neural network

Step 8. Spectra classified using an artificial neural network pattern recognition program. (This program is enabled on a parallel-distributed network of several personal computers [PCs] that facilitates optimization of neural network architecture). 

Wilkes, J. G. Rushing, L. Nayak, R. Buzatu, D. A. Sutherland, J. B. Rapid phenotypic characterization of Salmonella enterica strains by pyrolysis metastable atom bombardment mass spectrometry with multivariate statistical and artificial neural network pattern recognition. J. Microbiol. Meth. submitted for publication. 

Pattern Recognition of Artificial Neural Network to Waveform Data. 

Problems involving routine calculations are solved much faster and more reliably by computers than by humans. Nevertheless, there are tasks in which humans perform better, such as those in which the procedure is not strictly determined and problems which are not strictly algorithmic. One of these tasks is the recognition of patterns such as feces. For several decades people have been trying to develop methods which enable computers to achieve better results in these fields. One approach, artificial neural networks, which model the functionality of the brain, is explained in this section. 

Sometimes fuzzy logic controllers are combined with pattern recognition software such as artificial neural networks (Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1992). 

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. 

The brain s remarkable ability to learn through a process of pattern recognition suggests that, if we wish to develop a software tool to detect patterns in scientific or, indeed, any other kind of data, the structure of the brain could be a productive starting point. This view led to the development of artificial neural networks (ANNs). The several methods that are gathered under the ANN umbrella constitute some of the most widely used applications of Artificial Intelligence in science. Typical areas in which ANNs are of value include ... 

A generalised structure of an electronic nose is shown in Fig. 15.9. The sensor array may be QMB, conducting polymer, MOS or MS-based sensors. The data generated by each sensor are processed by a pattern-recognition algorithm and the results are then analysed. The ability to characterise complex mixtures without the need to identify and quantify individual components is one of the main advantages of such an approach. The pattern-recognition methods maybe divided into non-supervised (e.g. principal component analysis, PCA) and supervised (artificial neural network, ANN) methods also a combination of both can be used. 

KNN)13 14 and potential function methods (PFMs).15,16 Modeling methods establish volumes in the pattern space with different bounds for each class. The bounds can be based on correlation coefficients, distances (e.g. the Euclidian distance in the Pattern Recognition by Independent Multicategory Analysis methods [PRIMA]17 or the Mahalanobis distance in the Unequal [UNEQ] method18), the residual variance19,20 or supervised artificial neural networks (e.g. in the Multi-layer Perception21). 

Table 2. Pattern recognition of analytes by artificial neural network (ANN)...

Electronic noses provide new possibilities for monitor the state of a cultivation non-in-vasively in real-time. The electronic nose uses an array of chemical gas sensors that monitors the off-gas from the bioreactor. By taking advantage of the off-gas components different affinities towards the sensors in the array it is possible with the help of pattern recognition methods to extract valuable information from the culture in a way similar to the human nose. For example, with artificial neural networks, metabolite and biomass concentration can be predicted, the fermentability of a medium before starting the fermentation estimated, and the growth and production stages of the culture visualized. In this review these and other recent results with electronic noses from monitoring microbial and cell cultures in bioreactors are described. 

Presnell, S. R. Cohen, F. E. (1993). Artificial Neural Networks for Pattern Recognition in Biochemical Sequences. Amu Rev Biophys Biomol Struct 22,283-98. 

Artificial neural networks are versatile tools for a number of applications, including bioinformatics. However, they are not thinking machines nor are they black boxes to blindly feed data into with expectations of miraculous results. Neural networks are typically computer software implementations of algorithms, which fortunately may be represented by highly visual, often simple diagrams. Neural networks represent a powerful set of mathematical tools, usually highly nonlinear in nature, that can be used to perform a number of traditional statistical chores such as classification, pattern recognition and feature extraction. 

The major limitation of the simple perceptron model is that it fails drastically on linearly inseparable pattern recognition problems. For a solution to these cases we must investigate the properties and abilities of multilayer perceptrons and artificial neural networks. 

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