A Modified Constructive Fuzzy Neural Networks for Classification of Large-Scale and Complicated Data

2006 ◽  
pp. 14-19 ◽  
Author(s):  
Lunwen Wang ◽  
Yanhua Wu ◽  
Ying Tan ◽  
Ling Zhang
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural

Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function

2019 ◽  
Vol 28 (01) ◽  
pp. 1950003 ◽  
Author(s):  
Paulo Vitor de Campos Souza ◽  
Luiz Carlos Bambirra Torres ◽  
Augusto Junio Guimarães ◽  
Vanessa Souza Araujo
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Activation Function ◽  
Fuzzy Neural Networks ◽  
Activation Functions ◽  
Large Scale Problems ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Learning Machine ◽  
Intelligent Models

The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.

scholarly journals Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Nurullah Akkaya ◽  
Ersin Aytac ◽  
Irfan Günsel ◽  
Ahmet Çağman
Keyword(s):  
Neural Networks ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Computer Interface ◽  
Human Control ◽  
Fuzzy Neural ◽  
Physically Disabled People

The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.

scholarly journals Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

AI ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 92-116 ◽  
Author(s):  
Paulo Vitor de Campos Souza ◽  
Augusto Junio Guimarães ◽  
Thiago Silva Rezende ◽  
Vinicius Jonathan Silva Araujo ◽  
Vanessa Souza Araujo
Keyword(s):  
Neural Networks ◽  
Computer Networks ◽  
Intelligent Systems ◽  
Large Scale ◽  
Fuzzy Neural Networks ◽  
Security Attacks ◽  
Massive Datasets ◽  
Fuzzy Neural ◽  
High Level ◽  
And Training

The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and training of artificial neural networks to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In addition to verifying the accuracy of the model, fuzzy rules were obtained through knowledge from the massive datasets to form expert systems. The acquired rules allow the creation of intelligent systems in high-level languages with a robust level of identification of anomalies in Internet transactions, and the accuracy of the results of the test confirms that the fuzzy neural networks can act in anomaly detection in high-security attacks in computer networks.

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