Granular neural network

Johanson JR. A rolling theory for granular solids. Trans ASME J AppI Mcch 1965 32 842-8. Turkoglu M, Aydin I. Murray M, Sakr A. Modeling of a roller-compaction process using neural networks and genetic algorithms. Eur J Pharm Biopharm 1999 48(3) 239-45. 

Different batch adsorption processes were modeled using a multilayer fccdforwaKl neural network to predict water sorption [28] or adsorption of binary vapor mixtures [29]. Breakthrough parameters of an ion-exchange column [30] or a granular activated carbon fixed bed [31] were also predicted using the same kind of perceptions. 

Nandedkar AV, Biswas PK (2009) A reflex fuzzy min max neural network for granular data classilicatirui. IEEE Trans Neural Network 20(7) 1117—1134... 

The component techniques of soft computing are not competitive, but complementary. Much research has been done to study the ways this complementarity can be exploited. Each of the components has features to offer a potential partnership. Systems that have such a partnership are called hybrid systems . Fuzzy logic uses the concept of computing with words, it deals with imprecision and information granularity and is an important tool for approximate reasoning. Neural networks learn and adapt. Genetic algorithms make use of a systemized random search and are an important tool for optimization. These three may be combined in different ways, as described below. 

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