skip to main content
Ngôn ngữ:
Giới hạn tìm kiếm: Giới hạn tìm kiếm: Dạng tài nguyên Hiển thị kết quả với: Hiển thị kết quả với: Chỉ mục

Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Castillo, Oscar ;Melin, Patricia ;Pedrycz, Witold ;Kacprzyk, Janusz;; Kacprzyk, Janusz (Editor) ; Castillo, Oscar (Editor) ; Melin, Patricia (Editor) ; Pedrycz, Witold (Editor) ; Kacprzyk, Janusz (Editor)

Studies in Computational Intelligence

ISBN: 9783319051697 ; ISBN: 3319051695 ; E-ISBN: 9783319051703 ; E-ISBN: 3319051709 ; DOI: 10.1007/978-3-319-05170-3

Toàn văn sẵn có

Trích dẫn Trích dẫn bởi
  • Nhan đề:
    Recent Advances on Hybrid Approaches for Designing Intelligent Systems
  • Tác giả: Castillo, Oscar ; Melin, Patricia ; Pedrycz, Witold ; Kacprzyk, Janusz
  • Kacprzyk, Janusz (Editor) ; Castillo, Oscar (Editor) ; Melin, Patricia (Editor) ; Pedrycz, Witold (Editor) ; Kacprzyk, Janusz (Editor)
  • Chủ đề: Engineering ; Computational Intelligence ; Artificial Intelligence (Incl. Robotics) ; Engineering ; Computer Science
  • Là 1 phần của: Studies in Computational Intelligence
  • Mô tả: Intro -- Preface -- Contents -- Part I Type-2 Fuzzy Logic -- 1 Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers -- Abstract -- 1…Introduction -- 2…Theoretical Basis and Problem Statement -- 2.1 Type-2 Non-singleton Type-2 Fuzzy Logic Systems -- 2.1.1 Rules -- 2.1.2 Interval Type-2 Non-singleton TSK Interval Type-2 Fuzzy Inference System -- 2.2 Genetic Algorithms (GA) -- 2.3 Problem Statement -- 2.3.1 Fuzzy Logic Control Design -- 3…Simulation Results -- 3.1 Behavior of the Type-2 Non-singleton Fuzzy Logic Controller Under Perturbation -- 3.1.1 Perturbation: Pulse Generator -- 3.1.2 Perturbation: Equation -- 4…Conclusions -- References -- 2 Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control -- Abstract -- 1…Introduction -- 2…Basic Concepts -- 2.1 Modular Neural Networks -- 2.2 Type-2 Fuzzy Logic -- 2.3 Hierarchical Genetic Algorithms -- 3…Proposed Method -- 3.1 Application to Pattern Recognition -- 3.1.1 Databases -- 3.2 Application to Fuzzy Control -- 4…Experimental Results -- 4.1 Human Recognition -- 4.1.1 Face -- 4.1.2 Iris -- 4.1.3 Ear -- 4.1.4 Voice -- 4.1.5 Non-optimized Fuzzy Integration -- 4.1.6 Optimized Fuzzy Integration -- 4.2 Fuzzy Control -- 5…Conclusions -- References -- 3 Designing Type-2 Fuzzy Systems Using the Interval Type-2 Fuzzy C-Means Algorithm -- Abstract -- 1…Introduction -- 2…Overview of Interval Type-2 Fuzzy Sets -- 3…Interval Type-2 Fuzzy C-Means algorithm -- 4…Designing Type-2 Fuzzy Inference Systems Using Interval Type-2 Fuzzy C-Means Algorithm -- 4.1 Type-2 Fuzzy Systems Designed for Time Series Prediction -- 4.2 Type-2 Fuzzy Systems Designed for Classification of the Dataset Iris Flower -- 5…Conclusions -- References -- 4 Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning with Gaussian Membership Functions -- Abstract -- 1…Introduction -- 2…Background and Basic Concepts -- 2.1 Neural Network -- 2.2 Overview of Related Works -- 3…Proposed Method and Problem Description -- 4…Neural Network Architecture with Type-1 Fuzzy Weights -- 5…Neural Network Architecture with Type-2 Fuzzy Weights -- 6…Simulation Results -- 7…Conclusions -- References -- 5 A Comparative Study of Membership Functions for an Interval Type-2 Fuzzy System used to Dynamic Parameter Adaptation in Particle Swarm Optimization -- Abstract -- 1…Introduction -- 2…Methodology -- 3…Experimentation with the Fuzzy Systems and the Benchmark Mathematical Functions -- 4…Conclusions -- References -- 6 Genetic Optimization of Type-2 Fuzzy Integrators in Ensembles of ANFIS Models for Time Series Prediction -- Abstract -- 1…Introduction -- 2…Background and Basic Concepts -- 2.1 ANFIS Models -- 2.2 Ensemble Learning -- 2.3 Interval Type-2 Fuzzy -- 2.4 Genetic Algorithms -- 2.5 Time Series -- 3…General Architecture of the Proposed Method -- 4…Simulations Results -- 4.1 Genetic Optimization of Interval Type-2 Fuzzy Integration Using Gaussian MFs -- 4.2 G.
  • Nơi xuất bản: Cham: Springer International Publishing
  • Năm xuất bản: 2014
  • Ngôn ngữ: English
  • Số nhận dạng: ISBN: 9783319051697 ; ISBN: 3319051695 ; E-ISBN: 9783319051703 ; E-ISBN: 3319051709 ; DOI: 10.1007/978-3-319-05170-3

Đang tìm Cơ sở dữ liệu bên ngoài...