Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data

2015 ◽  
Vol 45 (12) ◽  
pp. 2668-2679 ◽  
Author(s):  
Gopikrishna Deshpande ◽  
Peng Wang ◽  
D. Rangaprakash ◽  
Bogdan Wilamowski
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Data ◽  
Neural Network Architecture ◽  
Hyperactivity Disorder ◽  
Artificial Neural Network Architecture ◽  
Fully Connected

Introducing Asymmetry into Interneuron Learning

1995 ◽  
Vol 7 (6) ◽  
pp. 1191-1205 ◽  
Author(s):  
Colin Fyfe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Negative Feedback ◽  
Principal Components ◽  
Network Architecture ◽  
Hebbian Learning ◽  
Principal Component ◽  
Neural Network Architecture ◽  
Weight Decay ◽  
Artificial Neural Network Architecture

A review is given of a new artificial neural network architecture in which the weights converge to the principal component subspace. The weights learn by only simple Hebbian learning yet require no clipping, normalization or weight decay. The net self-organizes using negative feedback of activation from a set of "interneurons" to the input neurons. By allowing this negative feedback from the interneurons to act on other interneurons we can introduce the necessary asymmetry to cause convergence to the actual principal components. Simulations and analysis confirm such convergence.

scholarly journals Assessment of the Average Level of TOEFL Score by using SOM (Self Organizing Map) and K-Mean Clustering Techniques

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2593-2599
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Network Architecture ◽  
Prediction Errors ◽  
Self Organizing Map ◽  
Neural Network Architecture ◽  
Training Techniques ◽  
Artificial Neural Network Architecture ◽  
Industry Sectors ◽  
Economic Development Strategies

Economic growth as measured by GDP growth rates and economic growth set as an increase in GDP strongly helps government predictions about the economic situation and the formation of economic development strategies. This measurement is done by combining mathematical and computer technology to make qualitative and quantitative predictions scientifically and appropriately for economic growth trends. It is a good practical sense to use scientific and proven methods to predict future GDP development trends of a particular economy. In some cases, machine learning methods have proven to be better forecasting results than statistical methods. A Deep Neural Network (DNN) is one type of ANN (Artificial Neural network) architecture based on deep MLP (Multi Layer Perceptron), which uses Deep Learning training techniques. This study proposes the use of DNN to predict the percentage of GDP distribution at current prices by industry sector. In this case, the DNN used will have multiple outputs as many industry sectors. The aim of this study is how to predict for the next period with the smallest possible prediction errors by using DNN.

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