Abstract
About a decade ago, artificial neural networks (ANN) have been introduced to chemometrics for solving problems in analytical chemistry. ANN are based on the functioning of the brain and can be used for modeling complex relationships within chemical data. An ANN-model can be obtained by earning or training with examples. The model can be realized without any a priory theoretical assumptions about the associations in the data, as is the case for parametric physical or chemical models. The universal applicability, the simple concept and the impressive modeling capability have contributed to the enormous number of successful applications in analytical chemistry, published in the last ten years. In the literature, various paradigms of neural networks, based on different biological and psychological concepts, have been elaborated. The most familiar and the most frequently used concept is given by the multi-layer feed-forward neural network also referred as the error back-propagation network. The MLF-network, which is the central objective in this thesis, can be used as a non-parametric method for modeling nonlinear relations in chemical data. MLF-networks are commonly trained by means of the generalized delta learning rule which can be considered as an iterative least mean squares method. The generalized delta learning rule has proven to be robust and moreover generally applicable for different network-configurations. However, slow learning behavior is frequently observed, yielding unacceptable long training times. Especially for chemical problems where extensive numbers of variables (f.e. spectra) or objects (f.e. images) are used, the slow learning behavior becomes a serious drawback. The last five years, a lot of fundamental research has been conducted on faster and more efficient algorithms for training MLF networks. In this thesis, some improved training methods have been described and applied on a number of chemical problems.
