Artificial neural networks based models training

Recently, a new approach called artificial neural networks (ANNs) is assisting engineers and scientists in their assessment of fuzzy information, Polymer scientists often face a situation where the rules governing the particular system are unknown or difficult to use. It also frequently becomes an arduous task to develop functional forms/empirical equations to describe a phenomena. Most of these complexities can be overcome with an ANN approach because of its ability to build an internal model based solely on the exposure in a training environment. Fault tolerance of ANNs has been found to be very advantageous in physical property predictions of polymers. This chapter presents a few such cases where the authors have successfully implemented an ANN-based approach for purpose of empirical modeling. These are not exhaustive by any means. 

Bodor, et al. [42] compare the use of artificial neural networks with regression analysis techniques for the development of predictive solubility models. They report that the performance of the neural network model is superior to the regression-based model. Their study is based on a training set of 331 compounds. The model requires a diverse set of molecular descriptors to account for the structural variety in the training compounds. 

The total plant profitability model is configured in an ANN (artificial neural network) format, which is self-correcting and can be trained on the actual performance data of the plant based on past operation. 

Artificial neural networks (ANNs) are computer-based systems that can learn from previously classified known examples and can perform generalized recognition - that is, identification - of previously unseen patterns. Multilayer perceptions (MLPs) are supervised neural networks and, as such, can be trained to model the mapping of input data (e.g. in this study, morphological character states of individual specimens) to known, previously defined classes. 

Artificial neural networks train best and learn to generalize best if they are presented with good examples of the classes that they are trying to model, especially if based on many examples showing variations representative of those classes the net is attempting to discriminate. Herbarium specimens can provide much data of this kind and are also a primary source of information for taxonomists. The use of neural networks as tools for herbarium systematics is, therefore, to be... 

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