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Identification of Fake Green Tea by Sensory Assessment and Electronic Tongue
2015
Food science and technology research
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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
2012
International Journal of Information and Network Security (IJINS)
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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
2003
Sensors and actuators. B, Chemical
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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
2021
Contrast Media & Molecular Imaging
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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
2021
Review of Economics and Political Science
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(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
2017
Journal of Electrical Engineering and Technology
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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
2002
BioMedical Engineering OnLine
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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
2008
2008 First International Conference on Emerging Trends in Engineering and Technology
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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
2002
BioMedical Engineering OnLine
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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
1996
Computers in physics
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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
2018
MATEC Web of Conferences
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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
2003
Pattern Recognition
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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]
2010
arXiv
pre-print
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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
2013
Journal of Korea Multimedia Society
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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
2014
Ingeniare : Revista Chilena de Ingeniería
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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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