Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms: The Case of Face, Fingerprint and Voice Recognition

2009 ◽  
pp. 111-129 ◽  
Author(s):  
Ricardo Muñoz ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Voice Recognition ◽  
Modular Neural Networks

Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks

2013 ◽  
pp. 34-47
Author(s):  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Optimal Distribution ◽  
Modular Neural Networks ◽  
Novel Concept ◽  
The Individual

The complexity of problems has led to a shift toward the use of modular neural networks in place of traditional neural networks. The number of inputs to neural networks must be kept within manageable limits to escape from the curse of dimensionality. Attribute division is a novel concept to reduce the problem dimensionality without losing information. In this paper, the authors use Genetic Algorithms to determine the optimal distribution of the parameters to the various modules of the modular neural network. The attribute set is divided into the various modules. Each module computes the output using its own list of attributes. The individual results are then integrated by an integrator. This framework is used for the diagnosis of breast cancer. Experimental results show that optimal distribution strategy exceeds the well-known methods for the diagnosis of the disease.

Fuzzy Parameter Adaptation in Genetic Algorithms for the Optimization of Fuzzy Integrators in Modular Neural Networks for Multimodal Biometry

2020 ◽  
Vol 24 (3) ◽  
Author(s):  
Oscar Castillo ◽  
Denisse Hidalgo ◽  
Leticia Cervantes ◽  
Patricia Melin ◽  
Ricardo Martínez Soto
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Parameter Adaptation ◽  
Modular Neural Networks ◽  
Fuzzy Parameter
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