scholarly journals Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xue Han ◽  
Yan Zhao ◽  
Feng Wang ◽  
Zun Liu
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Fuzzy Theory ◽  
Sliding Window ◽  
Percentage Error ◽  
Variable Size ◽  
Monitoring Method ◽  
Status Monitoring ◽  
Musculoskeletal Rehabilitation ◽  
Rehabilitation Status

The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).

scholarly journals Influence of Dextransucrase of Weissella Cibaria Nitcsk4 on Low Molecular Weight Dextran Yield: a Statistical Approach using Mixed Level Taguchi Design and Artificial Neural Network

2019 ◽  
Vol 9 (2S2) ◽  
pp. 657-664
Keyword(s):  
Neural Network ◽  
Molecular Weight ◽  
Mean Squared Error ◽  
Sucrose Concentration ◽  
Average Molecular Weight ◽  
Taguchi Design ◽  
Percentage Error ◽  
Low Molecular Weight ◽  
Weight Average Molecular Weight ◽  
Weissella Cibaria

In the present study, the influence of dextransucrase of Weissella cibaria NITCSK4 (DSWc4), sucrose concentration, and reaction temperature on the yield of low molecular weight dextran (LMWD-DexWc4) was investigated using mixed level Taguchi design and back propagation neural network (BPNN). BPNN model with three neurons in a hidden layer generated a low mean squared error (MSE). The determination coefficients (R2 -value) for ANN and Taguchi models were 0.991 and 0.998, respectively. Considering absolute average deviation (AAD) and MSE, Taguchi model is more adequate. Among three factors, the percentage yield of low molecular weight of dextran is invariably dependent on the sucrose concentration. The study suggested that a low sucrose concentration (3% w/v), DSWc4 (0.25 IU/ml) and slightly high temperature (35°C) ultimately favored the production of LMWD-DexWc4 (91.639%). LMW-DexWc4 produced by DSWc4 at optimized conditions was analyzed. The weight average molecular weight of LMW-DexWc4 was calculated using M-H expression, found to be 85775 (≈90 kDa). The relative percentage error between the number and weight average molecular weight was found to be less (4.42%). The polydispersity (PD) index of the LMW-DexWc4 was found to be 0.9576 and the value is close to 1. The PD value depicted that the molecular weight distribution of dextran was narrowly dispersed.

scholarly journals Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

2009 ◽  
Vol 19 (2) ◽  
pp. 303-315 ◽  
Author(s):  
Krzysztof Siwek ◽  
Stanisław Osowski ◽  
Ryszard Szupiluk
Keyword(s):  
Neural Network ◽  
Power System ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Weighted Averaging ◽  
Percentage Error ◽  
Network Approach ◽  
Three Solutions ◽  
Neural Network Approach ◽  
Ensemble Neural Network

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power SystemThe paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.

Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

2014 ◽  
Vol 1044-1045 ◽  
pp. 1824-1827
Author(s):  
Yi Ti Tung ◽  
Tzu Yi Pai
Keyword(s):  
Neural Network ◽  
Low Income ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Real Gdp ◽  
Squared Error ◽  
Input Variables ◽  
Low Income Households

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

scholarly journals Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jeng-Fung Chen ◽  
Shih-Kuei Lo ◽  
Quang Hung Do
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Mean Squared Error ◽  
Transportation Systems ◽  
Wavelet Neural Network ◽  
Percentage Error ◽  
Short Term ◽  
Fuzzy Wavelet Neural Network

Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell’s method to advance the exploration capability and increase the convergence speed. Our presented approach combines the strengths of fuzzy logic, wavelet transform, neural network, and the heuristic algorithm to detect the trends and patterns of transportation data and thus has been successfully applied to transport forecasting. Other different forecasting methods, including ANN-based model, FWNN-based model, and WNN-based model, are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R). The performance indexes show that the FWNN model achieves lower RMSE and MAPE, as well as higherR, indicating that the FWNN model is a better predictor.

scholarly journals Prediction of rubber vulcanization using an artificial neural network

2021 ◽  
Vol 75 (5) ◽  
pp. 277-283
Author(s):  
Jelena Lubura ◽  
Predrag Kojic ◽  
Jelena Pavlicevic ◽  
Bojana Ikonic ◽  
Radovan Omorjan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Dense Layer ◽  
Ann Model ◽  
Different Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

scholarly journals A Predictive Framework for Electricity Consumption

2016 ◽  
Vol 6 (1) ◽  
pp. 25-35
Author(s):  
Patrick Ozoh ◽  
Shapiee Abd-Rahman ◽  
Jane Labadin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Electricity Consumption ◽  
Percentage Error ◽  
Adaptation Algorithm ◽  
Multiple Variables ◽  
Artificial Neural

This study investigates the performance of regression model, Kalman filter adaptation algorithm and artificial neural network to assess their qualities for predictions. It develops predictive algorithms based on price, temperature and humidity as multiple variables affecting time-varying aspect of electricity consumption. In order to meet energy demand through the use of electricity as an energy source for daily activities in buildings such as air conditioning, lighting, computers and cooking stoves., adequate allocation of energy resources and planning should be done, including predicting for electricity consumption. The process involves collecting data from the power grid of Faculty of Computer Science and Information Technology building, Universiti Malaysia Sarawak. The forecasting techniques were tested on the data collected, and the dataset consists of electricity consumption readings, with electricity price, humidity and temperature included in the forecasting model. The performances of regression model, artificial neural network and Kalman algorithm were tested using statistical evaluation parameters, root mean squared error (RMSE) and mean absolute percentage error (MAPE); while the parameter, standard deviation, was used to check the validity of models. This study identified Kalman algorithm as the most effective method of predicting consumption data compared to regression model, and artificial neural network.

scholarly journals An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2625 ◽  
Author(s):  
Roman Tkachenko ◽  
Ivan Izonin ◽  
Natalia Kryvinska ◽  
Ivanna Dronyuk ◽  
Khrystyna Zub
Keyword(s):  
Neural Network ◽  
Missing Values ◽  
Mean Squared Error ◽  
Ensemble Method ◽  
Transformation Model ◽  
General Regression Neural Network ◽  
Geometric Transformation ◽  
Percentage Error ◽  
General Regression Neural Networks ◽  
General Regression

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

scholarly journals PERBANDINGAN METODE REGRESI LINEAR DAN NEURAL NETWORK BACKPROPAGATION DALAM PREDIKSI NILAI UJIAN NASIONAL SISWA SMP MENGGUNAKAN SOFTWARE R

JOUTICA ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 331
Author(s):  
Masruroh Masruroh
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Output Data ◽  
Root Mean Squared Error ◽  
Absolute Percentage Error ◽  
Squared Error

Metode regresi linear dan neural network backpropagation merupakan metode yang kerap digunakan dalam model prediksi. Penelitian ini bertujuan untuk membandingkan akurasi metode regresi linear dan backpropagation dalam prediksi nilai Ujian Nasional siswa SMP. Data yang digunakan berupa data nilai ujian akhir semester dan ujian sekolah sebagai input dan nilai ujian nasional sebagai output. Data didapatkan dari SMPN 1 dan SMPN 2 Lamongan.. Jumlah dataset sebanyak 701 dibagi menjadi 75% data training dan 25% data testing. Simulasi prediksi dilakukan menggunakan software R. Parameter akurasi yang digunakan adalah Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan model prediksi menggunakan metode regresi linear menghasilkan RMSE sebesar 9,04 dan MAPE sebesar 3,94%, sedangkan model prediksi menggunakan backpropagation menghasilkan RMSE sebesar 7,28 dan MAPE sebesar 0,55%. Dengan demikian dalam penelitian ini metode neural network backpropagation memiliki akurasi yang lebih baik dalam prediksi nilai Ujian Nasional siswa SMP.

scholarly journals Prediction of COVID-19 Time Series – Case Studies of South Africa and Egypt using Interval Type-2 Fuzzy Logic System

2021 ◽  
Vol 10 (2) ◽  
pp. 627-635
Keyword(s):  
Neural Network ◽  
South Africa ◽  
Fuzzy Logic ◽  
Mean Squared Error ◽  
Fuzzy Logic System ◽  
Percentage Error ◽  
Neural Network Learning ◽  
Interval Type ◽  
Logic System

COVID-19 is a virus known to emanate from Wuhan, China in December 2019. COVID-19 spread widely to nearby countries like Japan and Korea, followed by Europe and America and later to Africa. Particularly, South Africa and Egypt have been worst hit by the virus. Generally, the COVID-19 data is highly uncertain and requires fuzzy logic approaches for the effective handling of these uncertainties. This study therefore presents the prediction of COVID-19 cases in South Africa and Egypt using interval type-2 fuzzy logic system with Takagi-Sugeno-Kang fuzzy inference and neural network learning. The parameters of the model are adapted using gradient descent backpropagation approach. The proposed model is found to outperform type-1 fuzzy logic system and artificial neural network in terms of the root mean squared error, mean absolute percentage error and mean absolute error

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