scholarly journals Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

2017 ◽  
Author(s):  
Moh Arozi ◽  
Farika T. Putri ◽  
Mochammad Ariyanto ◽  
Khusnul Ari M. ◽  
Munadi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Signal Recognition ◽  
Fuzzy Inference Systems ◽  
Emg Signal ◽  
Neuro Fuzzy ◽  
Inference Systems

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

Improving Weather Forecasting Using De-Noising with Maximal Overlap Discrete Wavelet Transform and GA Based Neuro-Fuzzy Controller

2019 ◽  
Vol 28 (03) ◽  
pp. 1950012 ◽  
Author(s):  
Ayşe Tuğba Dosdoğru
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Membership Function ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Weather Forecasting ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Function Type ◽  
Neuro Fuzzy

Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is one of the most important neuro-fuzzy systems. ANFIS can be successfully applied to different real-world problems. However, it is difficult to create the ANFIS structure whose parameters directly influence the solutions. Therefore, hybrid ANFIS methods are generally used to increase efficiency and adaptability. This paper used an integrated neuro-fuzzy controller that is also known as PATSOS. The main purpose of this study is to improve the performance of the PATSOS method for weather forecasting. Our proposed PATSOS method is different from the previous ones since it embeds Genetic Algorithm (GA) into the PATSOS and also de-noising with Maximal Overlap Discrete Wavelet Transform (MODWT) is used to improve the data quality. GA is employed to optimize the moving average type, moving average degree, and de-noising degree. Furthermore, epoch number, membership function type, and membership function number for the PATSOS are optimized by GA. The results obtained by the hybrid PATSOS method are presented and compared with different cities and different models. It is concluded that proposed hybrid method forecasts daily mean temperature accurately. Proposed GA based PATSOS method can also provide remarkable advantages for determining parameter values in other complex, dynamic and non-linear forecasting problems.

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