Computational Intelligence Based on Lattice TheoryVassilis G. Kaburlasos, Gerhard X. Ritter 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
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 |
Other editions - View all
Computational Intelligence Based on Lattice Theory Vassilis G. Kaburlasos,Gerhard X. Ritter No preview available - 2010 |
Computational Intelligence Based on Lattice Theory Vassilis G. Kaburlasos,Gerhard X. Ritter No preview available - 2007 |
Common terms and phrases
algebra algorithm analysis applications approach associative memory basis binary called chapter classifiers colour complete computed concept consider convex coordinates corresponding database dataset defined definition dendrites denote described distance domain element endmembers erosion example exemplar experiments expressed extended extraction function fuzzy lattice fuzzy set given hyperbox IEEE implication independent input Intelligence interval introduced layer learning logic mathematical matrix mean measure method morphisms morphological Neural Networks neuron noise Note objects obtained operators output pair parameter pattern performance positive prediction presented problem Proc proposed Proposition reasoning recall recognition refer region relation representation represented respectively rules Sect selected shown shows similar space standard basis step structure t-norm Table Theorem theory tion truth University valuation vector versions weights