Back-propagation artificial neural network

Nonmodel-based controllers, such as the least mean square (LMS) and artificial neural network back-propagation adaptive controllers, employ iterative approaches to update control parameters in real time [14-17]. However, those methods may encounter difficulties of numerical divergence and local optimiza-... 

Chen et al. (2008) employed a commercial electronic tongue, based on an array of seven sensors, to classify 80 green tea samples on the basis of their taste grade, which is usually assessed by a panel test. PCA was employed as an explorative tool, while fc-NN and a back propagation artificial neural network (BP-ANN) were used for supervised classification. Both the techniques provide excellent results, achieving 100% prediction ability on a test set composed of 40 samples (one-half of the total number). In cases like this, when a simple technique, such as fc-NN, is able to supply excellent outcomes, the utilization of a complex technique, like BP-ANN, does not appear justified from a practical point of view. 

Error Back-propagation Artificial Neural Networks... 

Sun et al. [74] employed a 3-layer artificial neural network model with a back-propagation error algorithm to classify wine samples in 6 different regions based on the measurements of trace amounts of B, V, Mn, Zn, Fe, Al, Cu, Sr, Ba, Rb, Na, P, Ca, Mg and K by ICP-OES. Similarly,... 

Artificial neural networks often have a layered structure as shown in Figure 8.2 (b). The first layer is the input layer. The second layer is the hidden layer. The third layer is the output layer. Learning algorithms such as back-propagation that are described in many textbooks on neural networks (Kosko 1992 Rumelhart and McClelland 1986 Zell 1994) may be used to train such networks to compute a desired output for a given input. The networks are trained by adjusting the weights as well as the thresholds. 

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. 

An example of a non-covalent MIP sensor array is shown in Fig. 21.14. Xylene imprinted poly(styrenes) (PSt) and poly(methacrylates) (PMA) with 70 and 85% cross-linker have been used for the detection of o- and p-xylene. The detection has been performed in the presence of 20-60% relative humidity to simulate environmental conditions. In contrast to the calixarene/urethane layers mentioned before, p-xylene imprinted PSts still show a better sensitivity to o-xylene. The inversion of the xylene sensitivities can be gathered with PMAs and higher cross-linker ratios. As a consequence of the humidity, multivariate calibration of the array with partial least squares (PLS) and artificial neural networks (ANN) is performed, The evaluated xylene detection limits are in the lower ppm range (Table 21.2), whereas neural networks with back-propagation training and sigmoid transfer functions provide the most accurate data for o- and p-xylene concentrations as compared to PLS analyses. 

Among the nonlinear methods, there are, besides nonlinear least squares regression, i.e. polynomial regression, the nonlinear PLS method. Alternating Conditional Expectations ACE), SMART, and MARS. Moreover, some Artificial Neural Networks techniques have also to be considered among nonlinear regression methods, such as the back-propagation method. 

The computational construction of artificial neural networks has also been applied to relate physicochemical parameters of benzodiazepines with their receptor affinity and to predict BZR properties and BZR ligand affinities. In a study by Maddalena and Johnston, back-propagation artificial neural networks were used to examine the QSAR between substituent constants at six positions on 57 ben-zodiazepinones with their empirically determined binding affinities (118). Among the findings of the study were the following ... 

Artificial neural networks (ANNs) represent, as opposed to PLS and MLR, a nonlinear statistical analysis technique [86]. The most commonly used N N is of the feed-forward back-propagation type (Figure 14.2). As is the case of both PLS and MLR, there are a few aspects of NN to be considered when using this type of analysis technique ... 

Artificial neural networks (ANNs) are programs designed to simulate the way a simple biological nervous system is believed to operate. They are based on simulated nerve cells or neurons that are joined together in a variety of ways to form networks. These networks have the capacity to learn, memorize and create relationships amongst data [307-313] or chemical characteristics [314-319]. There are many different types of ANNs that can be used in environmental forensic investigations, but some are more popular than others. The most widely used ANN is known as the Back Propagation ANN. This type of ANN is excellent at prediction and classification tasks. Another is the Kohonen or Self... 

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