Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

Ibrahimy, Muhammad Ibn; Ahsan, Md. Rezwanul; Khalifa, Othman O.

doi:10.2478/msr-2013-0023

Cited by 47 publications

(14 citation statements)

References 25 publications

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“…Wavelet neural network and multi-layer perceptrons were used to handle EMG signals in order to identify neuromuscular disorders [23,24]. Learning vector quantization, support vector machine, and Levenberg-Marquardt-based networks were applied to EMG signals for classifying hand-motion patterns [25][26][27]. EMG-based unsupervised competitive learning techniques were employed for the identification of the muscle activity during pregnancy [28].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

et al. 2019

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Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data. We propose a deep learning approach for surface electromyographic (sEMG)-based classification of stance/swing phases and prediction of the foot–floor-contact signal in more natural walking conditions (similar to everyday walking ones), overcoming constraints of a controlled environment, such as treadmill walking. To this aim, sEMG signals were acquired from eight lower-limb muscles in about 10.000 strides from 23 healthy adults during level ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. By means of an extensive evaluation, we show that using a multi layer perceptron to learn hidden features provides state of the art performances while avoiding features engineering. Results, indeed, showed an average classification accuracy of 94.9 for learned subjects and 93.4 for unlearned ones, while mean absolute difference ( ± S D ) between phase transitions timing predictions and footswitch data was 21.6 ms and 38.1 ms for heel-strike and toe off, respectively. The suitable performance achieved by the proposed method suggests that it could be successfully used to automatically classify gait phases and predict foot–floor-contact signal from sEMG signals during level ground walking.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

et al. 2019

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“…A process history-based model is expected to incorporate the effects of installation, modification, and overhaul. An ANN-based approach is found beneficial for creating the process history data-based model of the compressor, which is a complex nonlinear system [28]- [37]. The model can predict the output feature from the identified input features.…”

Section: Model Developmentmentioning

confidence: 99%

Artificial Neural Network based Process History Data Model for Gas Turbine Compressor Systems

M.R.*,

P.,

Jayaraj

2019

IJRTE

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Gas turbine-based power plants are found to play a vital role in electric power generation and act as spinning reserves for renewable electric power. A robust performance assessment tool is inevitable for a gas turbine system to maintain high operational flexibility, availability, and reliability at different operating conditions. A suitable simulation model of the gas turbine provides detailed information about the system operation under varying ambient and load conditions. This paper illustrates a systematic methodology for process history data-based modelling of a gas turbine compressor system. The ReliefF feature selection method is applied for the proper identification of the parameters influencing the compressor efficiency. Appropriate Artificial Neural Network (ANN) based models are developed for data classification and system modelling of the compressor. The model performance has been validated using actual plant operational data, and the standard deviation of the error in model output was found to be 0.38. A novel approach for suitable integration of data processing methods, machine learning tools and gas turbine domain knowledge has led to the development of a robust compressor model. The model has been utilized for the health assessment of an existing gas turbine compressor, demonstrated through an illustrative case study. The model has been found suitable for parametric analysis of compressor efficiency with operating hours, which is helpful for operational decision-making involving studies on the influence of part-load operation, compressor wash planning, maintenance planning etc

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“…The Levenberg-Marquardt learning algorithm [13] provided the change in weights in feedback. It is the fastest and most powerful algorithm for training in medium-scale feed-forward ANNs [14]. The data obtained from measuring 24 times a day during (2015-2018) period were used as input data with historical sequencing.…”

Section: Fig 1: Ann Model Designed For the Systemmentioning

confidence: 99%

Daily SO2 Air Pollution Prediction with the use of Artificial Neural Network Models

Gültepe¹,

Mine²

2018

IJCA

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Although Artificial Neural Networks have been used for many years, its use in air pollution analysis has become widespread in 10 years. In this study, an Artificial Neural Network model was proposed to predication air pollution in Kastamonu province of Turkey. In this study as an example of Kastamonu province, Artificial Neural Network model was formed by using a pollution parameter (PM10) and 5 different meteorological factors (air temperature, air pressure, humidity, wind direction and wind speed) which were measured daily data during (2015-2018) period. It is aimed to propose refined model to predict the value of air pollution concentration (SO 2) after 24 hours by using this model. In other words, the air quality model is predicted. Artificial Neural Network is very successful compared to the new and classical statistical methods. Feed back-propagation algorithm has been used in all developed Artificial Neural Network model. The data set used in this study is divided into three subsets including training, validations and test data sets. The first 70% percent of the data set were used as the training subset, 15% of the data set used as the test set and 15% of the data set were used in validation set. The Mean Squeare Error (MSE) was measured for the performance of the network. It was observed that the developed ANN model was in agreement with the experimental results.

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Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

Cited by 47 publications

References 25 publications

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

Artificial Neural Network based Process History Data Model for Gas Turbine Compressor Systems

Daily SO2 Air Pollution Prediction with the use of Artificial Neural Network Models

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