Kaburlasos V.G., Ritter G.X. (eds.) Computational Intelligence Based on Lattice Theory

Springer, 2007. — 381 p.This book presents the latest applications of lattice theory in Computational Intelligence (CI). The book focuses on neural computation, mathematical morphology, machine learning, and (fuzzy) inference/logic. The book comes out of a special session held during the World Council for Curriculum and Instruction World Conference (WCCI 2006). The articles presented here demonstrate how lattice theory may suggest viable alternatives in practical clustering, classification, pattern analysis, and regression applications.Part I Neural Computation
Granular Enhancement of Fuzzy-ART/SOM Neural Classifiers Based on Lattice Theory
Learning in Lattice Neural Networks that Employ Dendritic Computing
Orthonormal Basis Lattice Neural Networks
Generalized Lattices Express Parallel Distributed Concept Learning
Part II Mathematical Morphology Applications
Noise Masking for Pattern Recall Using a Single Lattice Matrix Associative Memory
Convex Coordinates From Lattice Independent Sets for Visual Pattern Recognition
A Lattice-Based Approach to Mathematical Morphology for Greyscale and Colour Images
Morphological and Certain Fuzzy Morphological Associative Memories for Classification and Prediction
Part III Machine Learning Applications
The Fuzzy Lattice Reasoning (FLR) Classifier for Mining Environmental Data
Machine Learning Techniques for Environmental Data Estimation
Application of Fuzzy Lattice Neurocomputing (FLN) in Ocean Satellite Images for Pattern Recognition
Genetically Engineered ART Architectures
Fuzzy Lattice Reasoning (FLR) Classification Using Similarity Measures
Part IV Logic and Inference
Fuzzy Prolog: Default Values to Represent Missing Information
Valuations on Lattices: Fuzzification and its Implications
L-fuzzy Sets and Intuitionistic Fuzzy Sets
A Family of Multi-valued t-norms and t-conorms
The Construction of Fuzzy-valued t-norms and t-conorms