Classification of Multi-Types of EEG Time Series Based on Embedding Dimension Characteristic Parameter

We have studied the detection of epileptic seizure by EEG signals based on embedding dimension as the input characteristic parameter of artificial neural networks has been studied in the research before. The results of the experiments showed that the overall accuracy as high as 100% can be achieved for distinguishing normal and epileptic EEG time series. In this paper, classification of multi-types of EEG time series based on embedding dimension as input characteristic parameter of artificial neural network will be studied, and the probabilistic neural network (PNN) will be also employed as the classifier for comparing the results with those obtained before. Cao’s method is also applied for computing the embedding dimension of normal and epileptic EEG time series. The results show that different types of EEG time series can be classified using the embedding dimension of EEG time series as characteristic parameter when the number of feature points exceed some value, however, the accuracy were not satisfied up to now, some work need to be done to improve the classification accuracy.

Application of BP Neural Network to Extract AE Characteristic Parameters of the Crack in the Drawing Parts

Using BP Neural Network to optimize AE characteristic parameters of crack in drawing parts.By detecting the optimized characteristic parameters of crack, the crack in drawing parts are identified.According to the quality of drawing parts,the output of the network are crack signal and normal signal.Comparing the sensitivity of the input characteristic parameters on the output characteristic parameters,then pick the characteristic parameters which have bigger sensitivity values.Finally,the AE characteristic parameters such as Rise Time、AE Event Counter、Energy、Amplitude、Frequency Centroid can represent the signal of crack in the drawing parts better.These five characteristic parameters can identify the crack signal in the forming process of the drawing parts.