Multilayer perceptron artificial neural

Anderson et al used LIBS spectra and three multivariate methods to perform quantitative chemical analysis of rocks. The methods used were PLS, multilayer perceptron artificial neural networks (MLP ANNs) and cascade correlation (CC) ANNs. Precision and accuracy were influenced by the ratio of laser beam diameter (490 pm) to grain size, with coarse-grained rocks often resulting in lower accuracy and precision than analyses of fine-grained rocks and powders. 

Figure6.25 Schematicdrawingofan artificial neural network with a multilayer perceptron topology, showing the pathways from the input Xj to the output y , and the visible and hidden node layers.

Fig. 17. Use of a multilayer perceptron-type artificial neural network to analyze an interferometric image of...

Figure 5.3 Simple multilayered perceptron of an artificial neural network...

In the previous chapter a simple two-layer artificial neural network was illustrated. Such two-layer, feed-forward networks have an interesting history and are commonly called perceptrons. Similar networks with more than two layers are called multilayer perceptrons, often abbreviated as MLPs. In this chapter the development of perceptrons is sketched with a discussion of particular applications and limitations. Multilayer perceptron concepts are developed applications, limitations and extensions to other kinds of networks are discussed. 

One of the early problems with multilayer perceptrons was that it was not clear how to train them. The perception training rule doesn t apply directly to networks with hidden layers. Fortunately, Rumelhart and others (Rumelhart et al 1986) devised an intuitive method that quickly became adopted and revolutionized the field of artificial neural networks. The method is called back-propagation because it computes the error term as described above and propagates the error backward through the network so that weights to and from hidden units can be modified in a fashion similar to the delta rule for perceptions. 

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. 

The artificial neural network (ANN) based prediction model utilized in the present study is the multilayer perceptrons (MLPs). It is adopted as the benchmark to compare with the time-varying statistical models since it has been shown that the MLP architecture could approximate... 

Gardner, M. W. and Dorhng, S. R. Artificial neural networks (the multilays perceptron) - a review of apphca-tions in the atmosphere sciences. Atmospheric Environment 32(14) (1998), 2627-2636. 

The framework of presented intelligent multi-sensor system is reflected by its data processing flow as illustrated in Fig. 3. Diversified sensors in field and sophisticated algorithms make the system scalable and adaptive to different driving profiles and scenarios. Data sets of complementary sensors are synchronized on the same time base before being conveyed to feature computation components. Based on the outcome of feature computation selected data sets are fused on the Mature level to construct input vectors for pattern classification so as to detect driver drowsiness. The classifier being used in this work is built upon Artificial Neural Network (ANN) or, more particularly. Multilayer Perceptrons (MLP) with supervised training procedure. 

Artificial neural networks (ANNs) are a non-linear function mapping technique that was initially developed to imitate the brain from both a structural and computational perspective. Its parallel architecture is primarily responsible for its computational power. The multilayer perceptron network architecture is probably the most popular and is used here. 

A multilayer perceptron (MLP) is a feed-forward artificial neural network model that maps sets of input data onto a set of suitable outputs (Patterson 1998). A MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function. MLP employs a supervised learning techruque called backpropagation for training the network. MLP is a modification of the standard linear perceptron and can differentiate data that are not linearly separable. 

Boccorh RK, Paterson A (2002) An artificial neural network model for predicting flavour intensity in blackcurrant concentrates. Food Qual Prefer 13(2) 117-128 Ceballos-Magana SG, de Pablos F, Jurado JM, Martin MJ, Alcazar A, Muniz-Valencia R, Izquierdo-Homillos R (2013) Characterisation of tequila according to their major volatile composition using multilayer perceptron neural networks. Food Chem 136(3) 1309-1315... 

Keywords Artificial neural network, support vector machines, mathematical modeling, multilayer perceptron, hybrid modeling methodologies, pharmaceutical applications... 

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