Integrated processing method for microseismic signal based on deep neural network

A New Approach for Investigating the Complexity of Short Term EEG Signal Based on Neural Network

 Background and purpose: The nonlinear quality of electroencephalography (EEG), like other irregular signals, can be quantified. Some of these values, such as Lyapunovchr('39')s representative, study the signal path divergence and some quantifiers need to reconstruct the signal path but some do not. However, all of these quantifiers require a long signal to quantify the signal complexity. Mate...

A conjugate gradient based method for Decision Neural Network training

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...

Flexible Deep Neural Network Processing

The recent success of Deep Neural Networks (DNNs) has drastically improved the state of the art for many application domains. While achieving high accuracy performance, deploying state-of-the-art DNNs is a challenge since they typically require billions of expensive arithmetic computations. In addition, DNNs are typically deployed in ensemble to boost accuracy performance, which further exacerb...

Deep Neural Network-Based Method for Economic Dispatch in Microgrid

Image Retrieval Method for Deep Neural Network

Because of the large data in the image database, the key problem of the retrieval algorithm is to retrieve the required image in the short time. Aiming at this problem, this article given a self-learning deep belief neural network method, and through building layers, input, output, and self-learning algorithm in network architecture to get global algorithm for image retrieval. The accuracy and ...