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.

Klappentext

This eighteen-chapter 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.

Inhalt

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.