Computational Intelligence Based on Lattice Theory

Front Cover
Vassilis G. Kaburlasos, Gerhard X. Ritter
Springer, Jun 26, 2007 - Computers - 375 pages
A number of di?erent instruments for design can be uni?ed in the context of lattice theory towards cross-fertilization By“latticetheory”[1]wemean,equivalently,eitherapartialordering relation [2,3]ora couple of binary algebraic operations [3, 4]. There is a growing interest in computational intelligence based on lattice theory. A number of researchers are currently active developing lattice theory based models and techniques in engineering, computer and information s- ences, applied mathematics, and other scienti?c endeavours. Some of these models and techniques are presented here. However, currently, lattice theory is not part of the mainstream of com- tationalintelligence.Amajorreasonforthisisthe“learningcurve”associated with novel notions and tools. Moreover, practitioners of lattice theory, in s- ci?c domains of interest, frequently develop their own tools and/or practices without being aware of valuable contributions made by colleagues. Hence, (potentially) useful work may be ignored, or duplicated. Yet, other times, di?erent authors may introduce a con?icting terminology. The compilation of this book is an initiative towards proliferating est- lished knowledge in the hope to further expand it, soundly. There was a critical mass of people and ideas engaged to produce this book. Around two thirds of this book’s chapters are substantial enhancements of preliminary works presented lately in a three-part special session entitled “Computational Intelligence Based on Lattice Theory” organized in the c- text of the World Congress in Computational Intelligence (WCCI), FUZZ- IEEE program, July 16-21, 2006 in Vancouver, BC, Canada. The remaining book chapters are novel contributions by other researchers.
 

Contents

Granular Enhancement of FuzzyARTSOM Neural
3
References
20
Learning in Lattice Neural Networks that Employ Dendritic
24
Ritter Gonzalo Urcid 25
45
Generalized Lattices Express Parallel Distributed Concept
59
Noise Masking for Pattern Recall Using a Single Lattice
79
Gonzalo Urcid Gerhard X Ritter 81
101
A LatticeBased Approach to Mathematical Morphology
129
Machine Learning Techniques for Environmental Data
195
Application of Fuzzy Lattice Neurocomputing FLN
215
Genetically Engineered ART Architectures
233
Fuzzy Lattice Reasoning FLR Classification
263
Default Values to Represent Missing
286
Fuzzification and its Implications
309
Anestis G Hatzimichailidis Basil K Papadopoulos
325
A Family of Multivalued tnorms and tconorms
341

morphological and certain fuzzy morphological associative memories including
149
The Fuzzy Lattice Reasoning FLR Classifier for Mining
173
The Construction of Fuzzyvalued tnorms and tconorms
361
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