Neural networks Autoassociative

Kramer, M. A., Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37, 233-243 (1991). 

All of the studies above have used back propagation multilayer perceptrons and many other varieties of neural network exist that have been applied to PyMS data. These include minimal neural networks,117119 radial basis functions,114120 self-organizing feature maps,110121 and autoassociative neural networks.122123... 

Kramer, M. A. Autoassociative neural networks. Comput. Chem. Eng. 1992,16, 313-328. 

Autoassociative neural networks provide a special five-layer network structure (Figure 3.6) that can implement nonlinear PCA by reducing variable dimensionality and producing a feature space map that retains the maximum possible amount of information from the original data set [150]. Autoassociative neural networks use conventional feedforward connections and sigmoidal or linear nodal transfer functions. 

The network has three hidden layers, including a bottleneck layer which is of a smaller dimension than either the input layer or the output layer. The network is trained to perform an identity mapping by approximating the input information at the output layer. Since there are fewer nodes in the bottleneck layer than the input or output layers, the bottleneck nodes implement data compression and encode the essential information in the inputs for its reconstruction in subsequent layers. In the NLPCA framework and terminology, autoassociative neural networks seek to provide a mapping of the form... 

Figure 3.6. Network architecture for determination of / nonlinear factors using an autoassociative neural network, a indicates nodes with sigmoidal functions, indicates nodes with sigmoidal or linear functions [150].

MA Kramer. Autoassociative neural networks. Corn-put. Chem. Engg., 16(4) 313-328,1992. 

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