Electroencephalogram (EEG) is a tool used in the diagnosis of a common neurological disorder Epilepsy. Analysis of long recordings of EEG by visual inspection for epilepsy is quite a tedious process. In this paper, we...
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ISBN:
(纸本)9781479925834
Electroencephalogram (EEG) is a tool used in the diagnosis of a common neurological disorder Epilepsy. Analysis of long recordings of EEG by visual inspection for epilepsy is quite a tedious process. In this paper, we present an approach for automated epileptic seizure detection by employing Multi layer Perceptron Neural Network (MLPNN) classifier. Independent Component Analysis (ICA), a statistical tool is used for extraction of features. The ascertained signals are trained under supervision by making use of memory efficient and fast Scaled Conjugate Gradient (SCG) backpropagation algorithm. The data set is taken from a publicly available EEG database. The MLPNN is designed with the tan-sigmoid transfer function in the hidden layer and output layer. The network is analyzed using performance metric like Mean Square Error and confusion matrix. The best classification accuracy is about 100% for the overall dataset. This indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.
This paper presents an investigation on the performance of an active suspension system. After discussing a 4-DOF car suspension model, dynamic simulation of the system with an existing nonlinear classic controller cal...
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This paper presents an investigation on the performance of an active suspension system. After discussing a 4-DOF car suspension model, dynamic simulation of the system with an existing nonlinear classic controller called back-stepping method has been presented which had been previously introduced by Huang and Lin (2004). The model-based identification of the system is then preformed by the aid of the feed forward neural networks which are finally used for the corresponding fault detection process. Simulation results show that the proposed identification and fault detection approach is highly effective in evaluating the performance of an active suspension system.
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