scholarly journals Generalized regression neural network association with terahertz spectroscopy for quantitative analysis of benzoic acid additive in wheat flour

2019 ◽  
Vol 6 (7) ◽  
pp. 190485 ◽  
Author(s):  
Xudong Sun ◽  
Junbin Liu ◽  
Ke Zhu ◽  
Jun Hu ◽  
Xiaogang Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Benzoic Acid ◽  
Wheat Flour ◽  
Terahertz Spectroscopy ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Powder Samples ◽  
Generalized Regression

Investigations were initiated to develop terahertz (THz) techniques associated with machine learning methods of generalized regression neural network (GRNN) and back-propagation neural network (BPNN) to rapidly measure benzoic acid (BA) content in wheat flour. The absorption coefficient exhibited a maximum absorption peak at 1.94 THz, which generally increased with the content of BA additive. THz spectra were transformed into orthogonal principal component analysis (PCA) scores as the input vectors of GRNN and BPNN models. The best GRNN model was achieved with three PCA scores and spread value of 0.2. Compared with the BPNN model, GRNN model to powder samples could be considered very successful for quality control of wheat flour with a correlation coefficient of prediction ( r p ) of 0.85 and root mean square error of prediction of 0.10%. The results suggest that THz technique association with GRNN has a significant potential to quantitatively analyse BA additive in wheat flour.

scholarly journals Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1631 ◽  
Author(s):  
Zhuo Zhang ◽  
Fayu Sun ◽  
Qingling Li ◽  
Weiqiang Wang ◽  
Dedong Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Generalized Regression ◽  
Supercritical Carbon

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

Neural Network Based Modelling and GRA Coupled PCA Optimization of Hole Sinking Electro Discharge Micromachining

2014 ◽  
Vol 4 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Rajesh Kumar Porwal ◽  
Vinod Yadava ◽  
J. Ramkumar
Keyword(s):  
Neural Network ◽  
Removal Rate ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Point Of View ◽  
Operating Conditions ◽  
Process Performance

Determination of material removal rate (MRR), tool wear rate (TWR) and hole taper (Ta) is a challenging task for manufacturing engineers from the productivity and accuracy point of view of the symmetrical and nonsymmetrical holes due to hole sinking electro discharge micro machining (HS-EDMM) process. Thus, mathematical models for quick prediction of these aspects are needed because experimental determinations of process performances are always tedious and time consuming. Not only prediction but determination of optimum parameter for optimization of process performance is also required. This paper attempts to apply a hybrid mathematical approach comprising of Back Propagation Neural Network (BPNN) for prediction and Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) for optimization with multiple responses of HS-EDMM of Invar-36. Experiments were conducted to generate dataset for training and testing of the network where input parameters consist of gap voltage, capacitance of capacitor and the resulting performance parameters MRR, TWR and Ta. The results indicate that the hybrid approach is capable to predict process output and optimize process performance with reasonable accuracy under varied operating conditions of HS-EDMM. The proposed approach would be extendable to other configurations of EDMM processes for different material.

ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

2011 ◽  
Vol 11 (04) ◽  
pp. 897-915 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
CHANDAN CHAKRABORTY
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Normal Sinus Rhythm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Polynomial Kernel ◽  
Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

Multi-steps degradation process prediction for bearing based on improved back propagation neural network

2012 ◽  
Vol 227 (7) ◽  
pp. 1544-1553 ◽  
Author(s):  
Lin Mi ◽  
Wei Tan ◽  
Ran Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Principal Component ◽  
Degradation Process ◽  
Extraction Methods ◽  
Back Propagation Neural Network ◽  
Time Frequency ◽  
Process Prediction

Bearing degradation process prediction is extremely important in industry. This article proposed a new method to achieve multi-steps bearing degradation prediction based on an improved back propagation neural network model. Firstly, time domain and time–frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis is used to merge the original features and reduce the dimension, the typical sensitive features can be extracted. Then, based on the extracted features, the improved three-layer back propagation neural network model is constructed and trained for multi-steps bearing degradation process prediction. The phase space construction method is used to determine the embedding dimension of the back propagation neural network model. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology.

scholarly journals DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

2021 ◽  
Vol 2 (336) ◽  
pp. 121-124
Author(s):  
T. Zh. Mazakov ◽  
D. N. Narynbekovna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Future Perspective ◽  
Data Set

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

scholarly journals Dissolved Gases Forecasting Based on Wavelet Least Squares Support Vector Regression and Imperialist Competition Algorithm for Assessing Incipient Faults of Transformer Polymer Insulation

Polymers ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Yiyi Zhang ◽  
Xin Li ◽  
Jiake Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Dissolved Gases ◽  
Dissolved Gas ◽  
Simulated Case

A solution for forecasting the dissolved gases in oil-immersed transformers has been proposed based on the wavelet technique and least squares support vector machine. In order to optimize the hyper-parameters of the constructed wavelet LS-SVM regression, the imperialist competition algorithm was then applied. In this study, the assessment of prediction performance is based on the squared correlation coefficient and mean absolute percentage error methods. According to the proposed method, this novel procedure was applied to a simulated case and the experimental results show that the dissolved gas contents could be accurately predicted using this method. Besides, the proposed approach was compared to other prediction methods such as the back propagation neural network, the radial basis function neural network, and generalized regression neural network. By comparison, it was inferred that this method is more effective than previous forecasting methods.

scholarly journals A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4499 ◽  
Author(s):  
Hao Wei ◽  
Yu Gu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Economic Value ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Learning Machine

The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

Detection of Organic Pollutions Using Artificial Neural Networks and Domain Transform Techniques

2014 ◽  
Vol 610 ◽  
pp. 279-282
Author(s):  
Ling Gao ◽  
Shou Xin Ren
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Noise Removal ◽  
Generalized Regression Neural Network ◽  
Mathematical Methods ◽  
Artificial Neural ◽  
Generalized Regression

This paper presented a novel method for detection of organic pollutions based on artificial neural networks combining domain transform techniques. Domain transform techniques are mathematical methods that allow the direct mapping of information from one domain to another. The most effectively used domain transform technique is wavelet packet transform (WPT). Wavelet packet representations of signals provided a local timefrequency description and separation ability between information and noise. The quality of the noise removal can be further improved by using best-basis algorithm and thresholding operation. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system. Generalized regression neural network (GRNN) is a kind of ANN and is applied for overcoming the convergence problem met in back propagation training and facilitating nonlinear calculation. In the case a method named WPT-based generalized regression neural network (WPTGRNN) was used for analyzing overlapping spectra.

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