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Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control37,99 €![Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization]( "Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization")
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization110,99 €![Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization]( "Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization")
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization110,99 €![Recent Advances in Interval Type-2 Fuzzy Systems]( "Recent Advances in Interval Type-2 Fuzzy Systems")
Recent Advances in Interval Type-2 Fuzzy Systems37,99 €![Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic]( "Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic")
Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic106,99 €![Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic]( "Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic")
Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic74,99 €![New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic]( "New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic")
New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic37,99 €
In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types ofsoil. Both datasets show interesting features that makes them interesting for testing new classification methods.