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Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method
2008
American Journal of Applied Sciences
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The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN) so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on ...
It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable ...
Table 3 shows a summary of RBFNN and MLPNN testing results for the case with 43 input features. ...
doi:10.3844/ajassp.2008.705.713
fatcat:3f4q7jolibb5nnnbtanqpwmlcu
Shape and Texture Aware Facial Expression Recognition using Spatial pyramid Zernike moments and Law's Textures Feature Set
2021
IEEE Access
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Similarly the expressions fear, happy, sad and sur-VOLUME 4, 2016 2) case (ii): The experiment on subject dependent FER was continued with the next set of feature vectors. ...
The F V int_norm_2 framed for the training datasets of JAFFE and KDEF are provided to both MLPNN and RBFNN classifiers. ...
doi:10.1109/access.2021.3069881
fatcat:tmsgvmkmtndq3dibwr76btg7sa
A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose
2017
Sensors
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An inversion algorithm for MLPNN [26] and a Gaussian kernel based SVM [27] are shown to generate closed boundary around training data which classify two classes of data, i.e., one class inside the boundary ...
(MLPNN), are found to suffer from false classification errors of irrelevant odor data. ...
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. ...
doi:10.3390/s17092089
pmid:28895910
pmcid:PMC5620598
fatcat:43c5f46nzncydf7hdzpi6pbrpu
Multiple Classifier System for Remote Sensing Image Classification: A Review
2012
Sensors
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the design of remote sensing classifier ensemble. ...
Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple ...
(PAPD) and Natural Science Foundation of Jiangsu Province, China (BK2010182). ...
doi:10.3390/s120404764
pmid:22666057
pmcid:PMC3355439
fatcat:xeu6ougwsrhfpfoyvl4llqxw2m
Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition
2019
Journal of Ambient Intelligence and Humanized Computing
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The model is trained using the Localized Generalization Error and focuses on the generalization ability by considering both the training error and stochastic sensitivity measure. ...
This paper provides insights into the importance of model generalization abilities and an initial analysis of the limitation of deep Neural Networks with respect to sensor-based activity recognition. ...
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s12652-019-01246-w
fatcat:lmvjmbvp6rgufjyst6c7ntacdu
Hybrid PSO-RBFNN and Proposed Algorithms of DDDWT for the Heart Disease Classification
2021
Engineering and Technology Journal
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DDDWT for the elimination of noise, and the use of PSO and ANN methods to classify the output from the Electrocardiogram (EGGS). ...
This approach merged the global search power of the PSO algorithm with the high efficiency of RBFNN's local optimums, overcome the inconsistency of the PSO algorithm and the RBFNN downside, quickly leading ...
The hybrid algorithm based on PSO-RBFNN has been trained to classify the EGG signals for five cases of the heart diseases, and the results achieved high classification performance compared with the other ...
doi:10.30684/etj.v39i4a.1498
fatcat:mucw344c35cctd4a3lrfrrrnqu
Wavelet-based feature extraction using probabilistic finite state automata for pattern classification
2011
Pattern Recognition
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Real-time data-driven pattern classification requires extraction of relevant features from the observed time series as low-dimensional and yet information-rich representations of the underlying dynamics ...
The proposed method of pattern classification has been experimentally validated on laboratory apparatuses for two different applications: (i) early detection of evolving damage in polycrystalline alloy ...
As stated earlier, SVM, k-NN, rbfNN, and mlpNN have been used as pattern classifiers in both cases. ...
doi:10.1016/j.patcog.2010.12.003
fatcat:m4vikdeucjdelpenuz26qkqjiy
Indoor Localization based on Multiple Neural Networks
다중 인공신경망 기반의 실내 위치 추정 기법
2015
Journal of Institute of Control Robotics and Systems
다중 인공신경망 기반의 실내 위치 추정 기법
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The neural network ensemble has drawn much attention in various areas where one neural network fails to resolve and classify the given data due to its' inaccuracy, incompleteness, and ambiguity. ...
To evaluate the effectiveness of our proposed location estimation method, we conduct the numerical experiments using the TGn channel model that was developed by the 802.11n task group for evaluating high ...
For channel profiles D and E, fluorescent light effect is added to the generated MIMO channel matrix. ...
doi:10.5302/j.icros.2015.14.0126
fatcat:7tu5lgjpvjfrpkg3l3y23np4qq
Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms
2019
International Journal of Applied Mathematics Electronics and Computers
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The Backpropagation (BP) Algorithm is one of the most popular and effective model of ANNs. ...
However, since it uses gradient descent algorithm, which attempts to minimize the error of the network by moving gradient of the error curve, easily get trapped at local minima. ...
Acknowledgements We would like to thank to the doctors who shared their knowledge and experience and to the Department of Neurology of Selcuk University Hospital (Non-Invasive Clinical Research Ethics ...
doi:10.18100/ijamec.475090
fatcat:7an3sjyq6vf2xo34xw52nl7jsy
Epileptic Seizure Detection and Experimental Treatment: A Review
2020
Frontiers in Neurology
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One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. ...
Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the ...
In the case of MLPNN, 18 out of 204 focal seizure samples were classified as a generalized seizure (8.8% error rate for focal seizure), and 9 out of 47 generalized seizure samples were classified as a ...
doi:10.3389/fneur.2020.00701
pmid:32849189
pmcid:PMC7396638
fatcat:dnnl7tyninc6zkqciwmuwo3esu
EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION
2016
Zenodo
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Email is one of the most popular communication media in the current century; it has become an effective and fast method to share and information exchangeall over the world. ...
One important type of ANNs is the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam message. ...
In the proposed case SVD effect in reducing the of the output error, it can also be used to remove any RBF when its associated singular value had a small value or if the approximation error can't affect ...
doi:10.5281/zenodo.3257986
fatcat:7f6qkoaelfelfgxexnaeckrlta
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction
2016
The Scientific World Journal
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The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature ...
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities ...
[16] employed support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) for solving problems and treating unseen samples near the training ...
doi:10.1155/2016/2401496
pmid:27738649
pmcid:PMC5050670
fatcat:zqjwfp5qmvfcnfzrrp6qswcosi
On-line voltage stability assessment using radial basis function network model with reduced input features
2011
International Journal of Electrical Power & Energy Systems
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Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks. ...
The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. ...
Table 6 shows the comparison between the RBFNN and MLPNN output and result of conventional AC load flow for one particular condition along with ranking of contingencies. ...
doi:10.1016/j.ijepes.2011.06.008
fatcat:o4iz3ahsrjafrncr4a45gq4si4
A Survey on Classification of ECG Signal Study
2016
Communications on Applied Electronics
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In this paper, we discuss a survey of preprocessing, ECG database, feature extraction and classifiers. ...
Classification of ECG signal is one of the most important reason of diagnosing the heart diseases. ...
(b) Selection of suitable classifier is one of the issues because the accuracy of classifier depends on more than one parameter such as arrhythmia type, selected feature extraction method and arrhythmia ...
doi:10.5120/cae2016652467
fatcat:ahrvvxnwuvbxfl4dyjmkvyo3s4
AUTOMATIC IDENTIFICATION OF EPILEPTIC AND BACKGROUND EEG SIGNALS USING FREQUENCY DOMAIN PARAMETERS
2010
International Journal of Neural Systems
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The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. ...
Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. ...
In the present case, a learning constant, η = 0.9 (that controls the step size) was chosen by trial and error. ...
doi:10.1142/s0129065710002334
pmid:20411598
fatcat:kdqtsewhibgqzmgjq634d2sahu
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