Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design

Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

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Application of a self-organizing fuzzy neural network controller with group-based genetic algorithm to greenhouse

2011 Seventh International Conference on Natural Computation ◽

10.1109/icnc.2011.6022188 ◽

2011 ◽

Cited By ~ 2

Author(s):

Yuan Yao ◽

Kailong Zhang ◽

Xingshe Zhou

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fuzzy Neural Network ◽

Neural Network Controller ◽

Network Controller ◽

Fuzzy Neural ◽

Self Organizing

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Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm

Computers & Chemical Engineering ◽

10.1016/s0098-1354(03)00034-6 ◽

2003 ◽

Vol 27 (7) ◽

pp. 1011-1019 ◽

Cited By ~ 16

Author(s):

Taizo Hanai ◽

Toshihiko Ohki ◽

Hiroyuki Honda ◽

Takeshi Kobayashi

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fuzzy Neural Network ◽

Initial Conditions ◽

Polymerization Reaction ◽

Fuzzy Neural

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Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9117 ◽

2020 ◽

Vol 17 (6) ◽

pp. 2755-2762

Author(s):

Pramoda Patro ◽

Krishna Kumar ◽

G. Suresh Kumar

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Ant Colony Optimization ◽

Optimization Problem ◽

Fuzzy Neural Network ◽

Ant Colony ◽

Fuzzy Rules ◽

Fuzzy Neural ◽

Approximate Function ◽

Age Prediction

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

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