scholarly journals Wavelet Transforms based Fall Detection with Neuro-Fuzzy Systems based Feature Selection

2019 ◽  
Vol 8 (3) ◽  
pp. 7498-7502
Keyword(s):  
Feature Selection ◽  
Wavelet Transforms ◽  
Fuzzy Systems ◽  
Fall Detection ◽  
Wavelet Coefficients ◽  
Distribution Measurement ◽  
Neuro Fuzzy ◽  
Area Distribution ◽  
Minimum Number ◽  
Overlap Area

This study proposes a method to detect fall with minimum features selected by a non-overlap area distribution measurement (NADM) method. In preprocessing step, wavelet transforms were carried out to extract wavelet coefficients from dataset acquired by subjects. The NADM was used to select the minimum number of features from wavelet coefficients, and then 19 features were finally selected from the 33 features. The performance result of the fall detection was tested with 19 features, and then the sensitivity, accuracy, and specificity were shown to be 95%, 96.13%, and 97.25%, respectively

Rule Extraction From Neuro-fuzzy System for Classification Using Feature Weights

2020 ◽  
Vol 9 (2) ◽  
pp. 59-79
Author(s):  
Heisnam Rohen Singh ◽  
Saroj Kr Biswas
Keyword(s):  
Feature Selection ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
False Positive Rate ◽  
Linguistic Features ◽  
Neuro Fuzzy ◽  
Quantitative Accuracy ◽  
Positive Rate ◽  
Benchmark Datasets ◽  
Improved Accuracy

Recent trends in data mining and machine learning focus on knowledge extraction and explanation, to make crucial decisions from data, but data is virtually enormous in size and mostly associated with noise. Neuro-fuzzy systems are most suitable for representing knowledge in a data-driven environment. Many neuro-fuzzy systems were proposed for feature selection and classification; however, they focus on quantitative (accuracy) than qualitative (transparency). Such neuro-fuzzy systems for feature selection and classification include Enhance Neuro-Fuzzy (ENF) and Adaptive Dynamic Clustering Neuro-Fuzzy (ADCNF). Here a neuro-fuzzy system is proposed for feature selection and classification with improved accuracy and transparency. The novelty of the proposed system lies in determining a significant number of linguistic features for each input and in suggesting a compelling order of classification rules using the importance of input feature and the certainty of the rules. The performance of the proposed system is tested with 8 benchmark datasets. 10-fold cross-validation is used to compare the accuracy of the systems. Other performance measures such as false positive rate, precision, recall, f-measure, Matthews correlation coefficient and Nauck's index are also used for comparing the systems. It is observed from the experimental results that the proposed system is superior to the existing neuro-fuzzy systems.

scholarly journals Fall Detection for Elderly Person using Neuro-fuzzy System and Wavelet Transformation

2019 ◽  
Vol 8 (12) ◽  
pp. 1730-1733
Keyword(s):  
Performance Evaluation ◽  
Wavelet Transforms ◽  
Wavelet Transformation ◽  
Elderly Person ◽  
Fall Detection ◽  
Training Set ◽  
Test Set ◽  
Fuzzy Membership Functions ◽  
Neuro Fuzzy ◽  
And Training

This study proposes a new methodology to detect falls and non-falls using a Neural Network with Weighted Fuzzy Membership Functions (NEWFM). Dataset acquired from subjects was applied to NEWFM after carrying out wavelet transforms. In order to test the performance evaluation of the fall detection by the NEWFM, the dataset was separated test set and training set at 2 to 8 and 5 to 5 ratios to carry out experiments. Based on the performance evaluation of the NEWFM, the sensitivity, accuracy, and specificity were shown to be 94.67%, 91.86% and 89.41%, respectively when the test set to the training set at the ratio was 2 to 8 and 91%, 91% and 91%, respectively, when the test set to the training set at the ratio was 5 to 5. This study also compares the performance evaluation of backpropagation (BP) and that of NEWFM.

NEURO-FUZZY SYSTEMS APPLIED ON A FIXED-WING AIRCRAFT CONTROL

2019 ◽  
Author(s):  
Renata Bernardes ◽  
Bruno Luiz Pereira ◽  
Felipe Machini Malachias Marques ◽  
Roberto Mendes Finzi Neto
Keyword(s):  
Fuzzy Systems ◽  
Aircraft Control ◽  
Neuro Fuzzy

scholarly journals A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

2012 ◽  
Vol 05 (07) ◽  
pp. 477-482 ◽  
Author(s):  
Rafik Mahdaoui ◽  
Leila Hayet Mouss
Keyword(s):  
Fuzzy Systems ◽  
Neuro Fuzzy ◽  
Fault Prognosis

scholarly journals Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

2019 ◽  
Vol 9 (1) ◽  
pp. 23
Author(s):  
Julia Tholath Jose ◽  
Adhir Baran Chattopadhyay
Keyword(s):  
Fuzzy Systems ◽  
Short Circuit ◽  
Sudden Change ◽  
Continuous Functions ◽  
Input Power ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Fuzzy Algorithm ◽  
Neuro Fuzzy ◽  
Doubly Fed

Doubly fed Induction Generators (DFIGs) are quite common in wind energy conversion systems because of their variable speed nature and the lower rating of converters. Magnetic flux saturation in the DFIG significantly affect its behavior during transient conditions such as voltage sag, sudden change in input power and short circuit. The effect of including saturation in the DFIG modeling is significant in determining the transient performance of the generator after a disturbance. To include magnetic saturation in DFIG model, an accurate representation of the magnetization characteristics is inevitable. This paper presents a qualitative modeling for magnetization characteristics of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy systems with one hidden layer of Gaussian nodes are capable of approximating continuous functions with arbitrary precision. The results obtained are compared with magnetization characteristics obtained using discrete fourier transform, polynomial and exponential curve fitting. The error analysis is also done to show the effectiveness of the neuro fuzzy modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast learning convergence is observed and great performance in accuracy is achieved.

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