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

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B. Bass,G. Huang,Y. Yin,S. J. Cohenron’s membrane. Neuroscientists now believe that the basic computation units are dendrites, capable of computing simple logic functions. This paper discusses 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 n

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Seeking User Input in Inflow Forecastingorld problems. In this chapter a novel model of a lattice neural network (LNN) is presented. This new model generalizes the standard basis lattice neural network (SB-LNN) based on dendritic computing. In particular, we show how each neural dendrite can work on a different orthonormal basis than the

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Water Science and Technology Libraryzed lattice, or category, in which the concepts are related in hierarchical fashion by lattice-like links called concept morphisms. A concept morphism describes how an abstract concept can be used within a more specialized concept in more than one way as with “color”, which can appear in “apples” as

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T. Masumoto,H. Sato,K. Iwasaki,K. Shibuyacs and data mining. The purpose of this chapter is to introduce a new feature extraction process based on the detection of extremal points on the cloud of points that represent the high dimensional data sample. These extremal points are assumed to de.ne an approximation to the convex hull covering t

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