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Identification of Fake Green Tea by Sensory Assessment and Electronic Tongue

Yanjie Li, Jincan Lei, Dawei Liang
2015 Food science and technology research  
Artificial Neural Network (ANN), including back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) and radial basis function neural network (RBF), were used as an automatic  ...  Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used to assess the feasibility and effectiveness of discrimination of electronic tongue.  ...  Table 1 . 1 Identification results of LMBP and RBF models ANN, artificial neural network; MSE, mean square error; LMBP, back propagation neural network with the Levenberg-Marquardt training algorithm;  ... 
doi:10.3136/fstr.21.207 fatcat:uotoyqbmxnfkznsqk5jdojbo4m

Synthetic Feature Transformation with RBF neural network to improve the Intrusion Detection System Accuracy and Decrease Computational Costs

Saeid Asgari Taghanaki, Behzad Zamani Dehkordi, Ahmad Hatam, Behzad Bahraminejad
2012 International Journal of Information and Network Security (IJINS)  
For this aim, we combined LDA and PCA as feature transformation and RBF Neural Network as classifier. RBF Neural Net (RBF-NN) has a high speed in classification and low computational costs.  ...  Our results on KDDCUP99 shows our proposed method have better performance related to other feature transformation methods such as LDA, PCA, Kernel Discriminant Analysis (KDA) and Local Linear Embedding  ...  Then the second phase is to set up the detection model based on wavelet clustering.  ... 
doi:10.11591/ijins.v1i1.339 fatcat:hrypjddnffdrfat3okl2xu33hu

Quantitative and qualitative analysis of VOCs mixtures by means of a microsensors array and different evaluation methods

A.M. Taurino, C. Distante, P. Siciliano, L. Vasanelli
2003 Sensors and actuators. B, Chemical  
We show how linear technique, such as principal component analysis (PCA) algorithm can be used for inspecting data distribution in simple cases like cluster discrimination.  ...  Moreover, non-linear techniques for difficult regression problems, such as artificial neural networks (multi-layer perceptron (MLP) and radial basis function (RBF)) are needed in particular for the prediction  ...  Then a non-linear transformation on raw normalized data has been investigated with two neural network models.  ... 
doi:10.1016/s0925-4005(03)00241-7 fatcat:zwlymdymuzamplr4um3tk7ljtu

Intelligent Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization

Biao Liu, Baogao Tan, Lidi Huang, Jingxin Wei, Xulin Mo, Jintian Zheng, Hanchuan Luo, Yuvaraja Teekaraman
2021 Contrast Media & Molecular Imaging  
The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis. Result.  ...  The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology  ...  MRI Image Classification Model Based on RBF Neural Network RBF Neural Network Model. e independent variable in the activation function of the RBF neural network is the distance D between the input and  ... 
doi:10.1155/2021/4997329 pmid:34629992 pmcid:PMC8463255 fatcat:cmp6bdfr5ncvxid3nzuuysjdgi

Improving the classification accuracy using hybrid techniques

Mamdouh Abdel Aim Saad Mowafy, Walaa Mohamed Elaraby Mohamed Shallan
2021 Review of Economics and Political Science  
(PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results  ...  Design/methodology/approach This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis  ...  MCA and PCA and running the analysis based on their dimensions improves the performance of the FCM clustering method.  ... 
doi:10.1108/reps-10-2020-0161 fatcat:7zvcgezlpvgkjcet7blzgbxesi

Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and Data Preprocessing Techniques

Wei Huang, Sung-Kwun Oh
2017 Journal of Electrical Engineering and Technology  
There are generally three folds when developing neural network classifiers.  ...  Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions.  ...  Preprocessing part realized via PCA and LDA The preprocessing part is realized based on principal component analysis and linear discriminate analysis.  ... 
doi:10.5370/jeet.2017.12.2.911 fatcat:t37mukahm5dcdcgvrwbtioas4y

Bacteria classification using Cyranose 320 electronic nose

Ritaban Dutta, Evor L Hines, Julian W Gardner, Pascal Boilot
2002 BioMedical Engineering OnLine  
Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes.  ...  Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got  ...  Probabilistic Neural network (PNN) and Radial basis function network (RBF) paradigms.  ... 
pmid:12437783 pmcid:PMC149373 fatcat:rtiwkkxqv5bevjrezi4kjxo6r4

Face Recognition Using Principal Component Analysis and RBF Neural Networks

S. Thakur, J.K. Sing, D.K. Basu, M. Nasipuri, M. Kundu
2008 2008 First International Conference on Emerging Trends in Engineering and Technology  
In this paper, an efficient method for face recognition using principal component analysis (PCA) and radial basis function (RBF) neural networks is presented.  ...  After performing the PCA, the hidden layer neurons of the RBF neural networks have been modelled by considering intra-class discriminating characteristics of the training images.  ...  CONCLUSION This paper presents a face recognition method based on PCA and RBF neural networks.  ... 
doi:10.1109/icetet.2008.104 dblp:conf/icetet/ThakurSBNK08 fatcat:kfrkko7ourbxtbnqjhdwt5zghq

Bacteria classification using Cyranose 320 electronic nose

Ritaban Dutta, Evor L Hines, Julian W Gardner, Pascal Boilot
2002 BioMedical Engineering OnLine  
Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes.  ...  Method: Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and  ...  Probabilistic Neural network (PNN) and Radial basis function network (RBF) paradigms.  ... 
doi:10.1186/1475-925x-1-4 fatcat:ejynjjkljnhqhfhjo66km5bzea

Fundamentals of Artificial Neural Networks

Mohamad H. Hassoun, Nathan Intrator, Susan McKay, Wolfgang Christian
1996 Computers in physics  
Radial Basis Function (RBF) Networks 285 6.1.1 RBF Networks versus Backprop Networks 294 6.1.2 RBF Network Variations 296 6.2 Cerebeller Model Articulation Controller (CMAC) 301 6.2.1 CMAC Relation to  ...  Rule 92 3.3.4 Linsker's Rule 95 3.3.5 Hebbian Learning in a Network Setting: Principal-Component Analysis (PCA) 97 3.3.6 Nonlinear PCA 101 3.4 Competitive Learning 103 3.4.1 Simple Competitive  ... 
doi:10.1063/1.4822376 fatcat:oz3focb4lzbxhba2gghoy332xu

Research on the Prediction Model of Transformer Bidding

LI Ming, LIU Yan-hao, YUAN Yi-ping, ZHANG Shi-wen, J. Heled, A. Yuan
2018 MATEC Web of Conferences  
analysis (PCA) and artificial neural network (ANN) pre-tender estimate forecast model is proposed.  ...  The model uses PCA to preprocess the original high dimensional data, select principal components (PC) as the radial basis function (RBF) neural network's input.  ...  Specific, can based on the k-means clustering method calculating basis function center c i , thus solving the variance σ, when the base function of RBF neural network for Gauss function, the variance of  ... 
doi:10.1051/matecconf/201817301016 fatcat:ekvcd6itdvdihmfx34pdvdp6tq

A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition

Javad Haddadnia, Karim Faez, Majid Ahmadi
2003 Pattern Recognition  
The FHLA combines the gradient method and the linear least-squared method for adjusting the RBF parameters and the neural network connection weights.  ...  The method determines the number of hidden neurons in the RBFNN structure by using cluster validity indices with majority rule while the characteristics of the hidden neurons are initialized based on advanced  ...  [24] and the principle component analysis (PCA) [25] .  ... 
doi:10.1016/s0031-3203(02)00231-5 fatcat:cz77u63g7fb2hpvqjfjpfyyw24

Intelligent System for Speaker Identification using Lip features with PCA and ICA [article]

Anuj Mehra, Anupam Shukla, Mahender Kumawat, Rajiv Ranjan, Ritu Tiwari
2010 arXiv   pre-print
different Artificial Neural Network (ANN) such as Back Propagation (BP), Radial Basis Function (RBF) and Learning Vector Quantization (LVQ).  ...  In this paper, we have presented a detailed comparative analysis between Principle Component Analysis (PCA) and Independent Component Analysis (ICA) which are used for feature extraction on the basis of  ...  Fig. 2 . 2 ICA model and related methods. Fig. 3 . 3 Back Propagation Multi-layer neural network structure. Fig. 4 . 4 RBF network structure with one output.  ... 
arXiv:1004.4478v1 fatcat:x6heoucncze3dfyourvghfkph4

Face Recognition Based on Improved Fuzzy RBF Neural Network for Smar t Device

Eung-Joo Lee
2013 Journal of Korea Multimedia Society  
As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF  ...  First, face features are extracted by the principal component analysis (PCA) method.  ...  Suppose there are a  dimensional characteristic Based on the analysis above, all of the samples can be processed by fuzzification when they are inputted RBF neural network, multiple input-one output  ... 
doi:10.9717/kmms.2013.16.11.1338 fatcat:vtheaneojnfedd4hdtwmoa4mey

Feature reduction using a RBF network for classification of learning styles in first year engineering students

Oswaldo Velez-Langs
2014 Ingeniare : Revista Chilena de Ingeniería  
Feature reduction is the technique of selecting a subset of 'relevant' features for building robust learning models as in an artificial neural network.  ...  In this paper, the well-known Principal Component Analysis (PCA) approach is applied in order to tackle this phenomenon in the design of an ANN with Radial Basis Functions (RBF) to be applied to classify  ...  Sending Data to the Model of Radial Base Functions Network In this approach, the PCA is used to reduce an initial set of 183 samples, with 80 characteristics each, to a new one which contains the same  ... 
doi:10.4067/s0718-33052014000100013 fatcat:a7upktf32jbsfc433fsd2w44vi
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