Neural network and fuzzy modeling in the task of detecting human operator fatigue in automation systems

2021 ◽  
Author(s):  
M. Ya. Braginskii ◽  
D. V. Tarakanov ◽  
E. L. Shoshin
Keyword(s):  
Neural Network ◽  
Fuzzy Modeling ◽  
Human Operator ◽  
Automation Systems ◽  
Operator Fatigue

scholarly journals In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 20262-20272 ◽  
Author(s):  
Yousaf Saeed ◽  
Khalil Ahmed ◽  
Mahdi Zareei ◽  
Asim Zeb ◽  
Cesar Vargas-Rosales ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Modeling ◽  
Artificial Neural

Detection of clustered microcalcifications using fuzzy modeling and convolution neural network

1996 ◽  
Author(s):  
Shih-Chung B. Lo ◽  
Huai Li ◽  
Jyh-Shyan Lin ◽  
Akira Hasegawa ◽  
Osamu Tsujii ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Modeling ◽  
Convolution Neural Network

Applications of Neuro Fuzzy Technology in Consumer Electronics Products

1995 ◽  
Vol 7 (1) ◽  
pp. 2-8
Author(s):  
Ryu Katayama ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fuzzy Systems ◽  
Fuzzy Modeling ◽  
Consumer Electronics ◽  
Electronic Products ◽  
Industrial Systems ◽  
Consumer Electronic ◽  
Neuro Fuzzy ◽  
Self Tuning

In recent years, intelligent industrial systems and consumer electronic products have been widely and intensively developed. Fuzzy logic, neural network, and neuro fuzzy technology, which integrates both approaches, are now regarded as an effective method to realize such intelligent features. In this paper, a review of the fuzzy boom in the consumer electronics market of Japan is presented. Typical applications of home appliances using fuzzy logic and neuro fuzzy technology are then described. Finally, methods and tools for developing fuzzy systems such as self-tuning and fuzzy modeling are reviewed.

scholarly journals ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR

2017 ◽  
Vol 12 ◽  
pp. 99
Author(s):  
Martin Ruzek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Human Operator ◽  
Biological Neural Network ◽  
Artificial Learning ◽  
Artificial Neural

This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network. The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system. The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition.

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