Titlebook: Computational Intelligence Based on Lattice Theory; Vassilis G. Kaburlasos,Gerhard X. Ritter Book 2007 Springer-Verlag Berlin Heidelberg 2

換話題

Finasteride

膠狀

套索

A. W. Heemink,H. F. P. Van Den Boogaardethodologies, the Fuzzy Lattice Neurocomputing (FLN) and the Support Vector Regression (SVR). The results of the speci.c applications are compared with past work on the same data set, and a discussion upon the exhibited features is carried out.

cornucopia

More on the Mathematical Framework,ness and uncertainty and the few examples that we can find are used by a minority. To extend a popular system (which many programmers are using) with the ability of combining crisp and fuzzy knowledge representations seems to be an interesting issue.

隱士

Learning in Lattice Neural Networks that Employ Dendritic Computingscusses two types of neural networks that take advantage of these new discoveries. The focus of this paper is on some learning algorithms in the two neural networks. Learning is in terms of lattice computations that take place in the dendritic structure as well as in the cell body of the neurons used in this model.

使人入神

dysphagia

Morphological and Certain Fuzzy Morphological Associative Memories for Classification and Prediction fuzzy case in view of the fact that a gray-scale MAM model can be converted into a fuzzy MAM model that coincides with the Lukasiewicz IFAM by applying an appropriate threshold. The article includes experimental results concerning applications of MAM and fuzzy MAM models in classiffication and prediction.

類型

暫停,間歇

Fuzzy Prolog: Default Values to Represent Missing Informationness and uncertainty and the few examples that we can find are used by a minority. To extend a popular system (which many programmers are using) with the ability of combining crisp and fuzzy knowledge representations seems to be an interesting issue.