Appliaction-driven neural networks are loosely tied to the biological realities.
As the knowledge on nervous systems is not advanced, we have to define
different functionalities and connection structures apart from a biological
perspective. The strength of application-driven neural networks hinges
upon three main characteristics:
These characteristics have played an important role in ANNs,
applicabilities to signal and image processing and analysis. An application
driven neural model can be very precisely defined. A NN
architecture comprises massively parallel adaptive processing elements
with hierarchically structured interconnection networks.
Adaptiveness and self-organization: it offers robust and adaptive
processing capabilities by adopting adaptive
learning and self-organization
Nonlinear network processing: it enhances the network's aproximation,
classification and noise-inmunity capabilities.
Parallel processing: it usually employs a large number of processing
cells enhanced by extensive interconnectivity.
Taxonomy of Neural Networks
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Artificial Neural Networks