Feedforward artificial neural network

Although the SOM is a type of neural network, its structure is very different from that of the feedforward artificial neural network discussed in Chapter 2. While in a feedforward network nodes are arranged in distinct layers, a SOM is more democratic—every node occupies a site of equal importance in a regular lattice. 

M-CASE/BAIA (see text). BP-ANN = three-layer feedforward artificial neural network trained by the backpropagation algorithm, PAAN = probabilistic artificial neural network, CPANN = counterpropagation artificial neural network. 

Feedforward artificial neural network (ANN) Radial Basis Perceptron ANN... 

Artificial neural networks arose from efforts to model the functioning of the mammalian brain. The most popular ANN — the feedforward ANN — has deeper roots in statistics than in neurobiology, though. A form of ANN (a Probability Neural Network) has been used within a QPA context to improve sensor data reliability, but not as an on-line quality model [57]. The best way to represent a feedforward ANN as an on-line quality model for SHMPC is... 

One of the most remarkable features of feedforward networks is the possibility to approximate, with an arbitrarily prescribed precision, even extremely complicated and extremely general dependencies [36-43]. In catalysis we are primarily interested in dependencies of catalyst performance, expressed as products yields, catalyst activity, conversion of feed molecules and products selectivity, on composition of the catalysts, their physical properties, and on reaction conditions. It is for the approximation of such dependencies that artificial neural networks have been used in catalysis so far [15-22]. 

For a detailed treatment of artificial neural networks, readers are again referred to specific monographs [35, 49-51], for a survey of their applications in chemistry to overview books [52, 53], reviews [54—56], and relevant sections of publications [57-59]. For heterogeneous catalysis, a recent overview has explained the applicability of feedforward networks to the approximation of unknown dependencies and to the extraction of logical rules from experimental data [22]. 

It is now well known that the artificial neural networks (ANNs) are nonlinear tools well suited to find complex relationships among large data sets [43], Basically an ANN consists of processing elements (i.e., neurons) organized in different oriented groups (i.e., layers). The arrangement of neurons and their interconnections can have an important impact on the modeling capabilities of the ANNs. Data can flow between the neurons in these layers in different ways. In feedforward networks no loops occur, whereas in recurrent networks feedback connections are found [79,80],... 

Application of artificial neural networks (ANN) for modelling of the kinetics of a catalytic hydrogenation reaction in a gas-liquid-solid system has been studied and discussed. The kinetics of the hydrogenation of 2,4-DNT over a palladium on alumina catalyst has been described with feedforward neural networks of dififerent architectures. A simple experimental procedure to supply learning data has been proposed. The accuracy and flexibility of the hybrid first principles-neural network model have been tested and compared with those of the classical model. 

Smits, J. R. M., Meissen, W. J., Buydens, L. M. C., and Kateman, G. (1994) Using artificial neural networks for solving chemical problems. Part I. Multilayer feedforward networks. Chemom. Intell. Lab. Syst. 22, 165-189. 

Russell, S. andNorvig, R, 1994. Artificial Intelligence A Modem Approach, Pve,niicQY a. Scarselli, F. and Tsoi, A., 1998. Universal approximation using feedforward neural networks A survey of some existing methods, and some new results. Neural Networks, 11 (1), 15-37. 

Like in the human brain, in artificial NNs the ability to process complex data is achieved by the interplay of a large number of neurons. In feedforward neural networks, the neurons are arranged in layers, as shown schematically for a small NN in Fig. 2. 

The ANNs were developed in an attempt to imitate, mathematically, the characteristics of the biological neurons. They are composed by intercoimected artificial neurons responsible for the processing of input-output relationships, these relationships are learned by training the ANN with a set of irqmt-output patterns. The ANNs can be used for different proposes approximation of functions and classification are examples of such applications. The most common types of ANNs used for classification are the feedforward neural networks (FNNs) and the radial basis function (RBF) networks. Probabilistic neural networks (PNNs) are a kind of RBFs that uses a Bayesian decision strategy (Dehghani et al., 2006). 

Keywords— Hypothyroid, Biomedical signal processing. Artificial intelligence. Feedforward neural networks. Particle Swarm Optimization. 

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