Optimum Decoder for Barni's Multiplicative Watermarking Based on Minimum Bayesian Risk Criterion

Dai, Yuewei; Wang, Jinwei; Liu, Guangjie

doi:10.1109/icnsc.2008.4525346

Cited by 2 publications

(3 citation statements)

References 9 publications

(6 reference statements)

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“…The cross-validation ideal means to group the original data, one part as a test set and the other part as a verification set, and using the training set to train the classifier firstly, then using the verification set to test the trained model to obtain the classification accuracy as a performance indicator for evaluating classifiers. The basic mathematical relationship of the objective function in a probabilistic neural network can be expressed as Equation (13).…”

Section: Results Of the Probabilistic Neural Optimizationmentioning

confidence: 99%

“…value = f (σ) (13) In the objective function, σ is the input variable, and value represents fitness. The actual meaning of value is the average classification accuracy under cross-validation.…”

Section: Results Of the Probabilistic Neural Optimizationmentioning

confidence: 99%

“…The essence of them is a parallel algorithm based on Bayesian minimum risk criterion. At the same time, unlike the traditional multi-layer forward network which needs to use the feedback algorithm to calculate the reverse error propagation, this method is the complete forward calculation process [13]. It has the advantages of short training time, less local optimum, and high classification accuracy.…”

Section: Fault Diagnosis Algorithm Selectionmentioning

confidence: 99%

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Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

Wang

et al. 2019

Applied Sciences

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Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.

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Section: Results Of the Probabilistic Neural Optimizationmentioning

confidence: 99%

Section: Results Of the Probabilistic Neural Optimizationmentioning

confidence: 99%

Section: Fault Diagnosis Algorithm Selectionmentioning

confidence: 99%

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