Enhanced hierarchical fuzzy model using evolutionary GA with modified ABC algorithm for classification problem

2015 ◽  
Author(s):  
Ting-Cheng Feng ◽  
Tsung-Ying Chiang ◽  
Tzuu-Hseng S. Li
Keyword(s):  
Fuzzy Model ◽  
Classification Problem ◽  
Abc Algorithm ◽  
Hierarchical Fuzzy Model

Design of hierarchical fuzzy model for classification problem using GAs

2006 ◽  
Vol 50 (1-2) ◽  
pp. 90-104 ◽  
Author(s):  
Nai Ren Guo ◽  
Tzuu-Hseng S. Li ◽  
Chao-Lin Kuo
Keyword(s):  
Fuzzy Model ◽  
Classification Problem ◽  
Hierarchical Fuzzy Model

Generating Singleton Fuzzy Models from Data with Interpretable Partition

2012 ◽  
Vol 629 ◽  
pp. 784-791 ◽  
Author(s):  
Juan Contreras
Keyword(s):  
Function Approximation ◽  
Least Squares Method ◽  
Fuzzy Model ◽  
Approximation Problem ◽  
Classification Problem ◽  
Descent Method ◽  
Recursive Least Squares ◽  
Gradient Descent Method ◽  
Recursive Least Squares Method ◽  
Triangular Membership Function

This paper presents a new methodology for obtaining singleton fuzzy model from experimental data. Each input variable is partitioned into triangular membership functions so that consecutive fuzzy sets exhibit and specific overlapping of 0.5. The recursive least squares method is employed to adjust the singleton consequences and the gradient descent method is employed to update only the modal value of each triangular membership function to preserve the overlap and reducing the number of parameters to be estimated. Applications to a function approximation problem and to a pattern classification problem are illustrated.

Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification

2017 ◽  
Vol 13 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Tutut Herawan ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Artificial Bee Colony ◽  
Classification Problem ◽  
Functional Link ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Learning Scheme ◽  
Feed Forward Network ◽  
Functional Link Neural Network

Functional Link Neural Network (FLNN) has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training. However, the standard Backpropagation (BP) learning uses for FLNN training prone to get trap in local minima which affect the FLNN classification performance. To recover the BP-learning drawback, this paper proposes an Artificial Bee Colony (ABC) optimization with modification on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN. This is motivated by good exploration and exploitation capabilities of searching optimal weight parameters exhibit by ABC algorithm. The result of the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared with the original FLNN architecture with standard Backpropagation (BP) (FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm provides better learning scheme for the FLNN network with average overall improvement of 4.29% as compared to FLNN-BP and FLNN-ABC.

Uneven Input Space Division and Balance of Generality and Conciseness of Submodels for Hierarchical Fuzzy Modeling

2000 ◽  
Vol 4 (2) ◽  
pp. 152-157
Author(s):  
Kanta Tachibana ◽  
◽  
Takeshi Furuhashi ◽  
Keyword(s):  
Fuzzy Neural Network ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Performance Indices ◽  
Promising Technique ◽  
Input Space ◽  
Fuzzy Models ◽  
Multiple Input ◽  
Fuzzy Neural ◽  
Hierarchical Fuzzy Model

Hierarchical fuzzy modeling is a promising technique to describe input-output relationships of nonlinear systems with multiple input. This paper presents a new method of dividing input spaces for hierarchical fuzzy modeling using the Fuzzy Neural Network (FNN) and Genetic Algorithm (GA). Uneven division of input space for each submodel in the hierarchical fuzzy model can be achieved with the proposed method. The obtained hierarchical fuzzy models are likely more concise and more precise than those identified with conventional methods. Studies on effects of the weights on performance indices of generality and conciseness of the fuzzy model are also shown in this paper.

Supporting decision-making in patient risk assessment using a hierarchical fuzzy model

2018 ◽  
Vol 20 (3) ◽  
pp. 477-488 ◽  
Author(s):  
Alessandro Jatobá ◽  
Hugo Cesar Bellas ◽  
Isabella Koster ◽  
Catherine M. Burns ◽  
Mario Cesar R. Vidal ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Fuzzy Model ◽  
Patient Risk ◽  
Hierarchical Fuzzy Model
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