Granular neural networks
We discuss an idea of granular computing regarded as a development environment of neural networks and leading to the emergence of a new class of granular neural networks. Such networks are viewed as new computing architectures that are focused on processing information granules rather than being geared towards plain numeric processing as usually encountered in most neural networks. The considered information granules are represented as constructs that may be formalized in the setting of set theory, fuzzy sets, rough sets or specified within a probabilistic environment. We discuss several main approaches to the design of information granules. A number of fundamental issues are tackled including specificity of information granules vis-a-vis learning complexity in the neural networks along with their generalization features. We also provide with a list of architectures of granular neural networks and elaborate on the associated training (learning) scenarios.
granular computing information granules information granules fuzzy sets neurocomputing information granulation interval analysis context-based clustering fuzzy neural networks