Computational Intelligence Based on Lattice Theory [electronic resource] / edited by Vassilis G. Kaburlasos, Gerhard X. Ritter.

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 -- 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 -- 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 -- 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.

The emergence of lattice theory within the field of computational intelligence (CI) is partially due to its proven effectiveness in neural computation. Moreover, lattice theory has the potential to unify a number of diverse concepts and aid in the cross-fertilization of both tools and ideas within the numerous subfields of CI. The compilation of this eighteen-chapter book is an initiative towards proliferating established knowledge in the hope to further expand it. This edited book is a balanced synthesis of four parts emphasizing, in turn, neural computation, mathematical morphology, machine learning, and (fuzzy) inference/logic. The articles here demonstrate how lattice theory may suggest viable alternatives in practical clustering, classification, pattern analysis, and regression applications.