Artificial neural networks based models example

Artificial-Neural-Network-Based Modeling Example of Fluid Catalytic Cracker... 

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... 

In general, simulation methods can be divided into those that use a computer and those that do not, as shown in Fig. 14.1. Those simulations without computer can be separated into destructive and nondestructive methods. Simulations that use a computer can either be based on technical models (for example, finite element method for structural composites, computational fluid dynamics for polymer flows), on examples taken from nature (for example, artificial neural networks, evolutionary algorithms for machine setting optimization), and those based on analytical equations (for example, warp tension during weaving). 

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