Blind separation of mixed-kurtosis signals using soft switch activation function

2007 ◽  
Vol 43 (17) ◽  
pp. 954 ◽  
Author(s):  
M.J. Zhang
Keyword(s):  
Activation Function ◽  
Blind Separation ◽  
Soft Switch ◽  
Switch Activation

BLIND SEPARATION OF MIXED KURTOSIS SIGNED SIGNALS USING PARTIAL OBSERVATIONS AND LOW COMPLEXITY ACTIVATION FUNCTIONS

2004 ◽  
Vol 04 (02) ◽  
pp. 207-223 ◽  
Author(s):  
KRISANA CHINNASARN ◽  
CHIDCHANOK LURSINSAP ◽  
VASILE PALADE
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Low Complexity ◽  
Activation Function ◽  
Data Sets ◽  
Blind Separation ◽  
Data Set ◽  
Partial Observations ◽  
On Line ◽  
High Computational Complexity

Although several highly accurate blind source separation algorithms have already been proposed in the literature, these algorithms must store and process the whole data set which may be tremendous in some situations. This makes the blind source separation infeasible and not realisable on VLSI level, due to a large memory requirement and costly computation. This paper concerns the algorithms for solving the problem of tremendous data sets and high computational complexity, so that the algorithms could be run on-line and implementable on VLSI level with acceptable accuracy. Our approach is to partition the observed signals into several parts and to extract the partitioned observations with a simple activation function performing only the "shift-and-add" micro-operation. No division, multiplication and exponential operations are needed. Moreover, obtaining an optimal initial de-mixing weight matrix for speeding up the separating time will be also presented. The proposed algorithm is tested on some benchmarks available online. The experimental results show that our solution provides comparable efficiency with other approaches, but lower space and time complexity.

Extranuclear effects of estrogen on cortical bone in males is dependent on estrogen receptor A activation function-1

2016 ◽  
Author(s):  
Helen Farman ◽  
Jianyao Wu ◽  
Karin Gustafsson ◽  
Sara Windahl ◽  
Sung Kim ◽  
...  
Keyword(s):  
Estrogen Receptor ◽  
Cortical Bone ◽  
Activation Function

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

A Riemannian approach to blind separation of t-distributed sources

2021 ◽  
Author(s):  
Florent Bouchard ◽  
Arnaud Breloy ◽  
Guillaume Ginolhac ◽  
Alexandre Renaux
Keyword(s):  
Blind Separation

Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks

2019 ◽  
Vol 12 (3) ◽  
pp. 156-161 ◽  
Author(s):  
Aman Dureja ◽  
Payal Pahwa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Primary Objective ◽  
Experimental Comparison ◽  
Activation Functions ◽  
Practical Applications ◽  
Network Activation ◽  
Non Linear ◽  
Hidden Layer

Background: In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. Objective: The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear problems. Methods: The configuration for this comparative model was used by using the datasets of 2 classes (Cat/Dog). The number of Convolutional layer used in this network was 3 and the pooling layer was also introduced after each layer of CNN layer. The total of the dataset was divided into the two parts. The first 8000 images were mainly used for training the network and the next 2000 images were used for testing the network. Results: The experimental comparison was done by analyzing the network by taking different activation functions on each layer of CNN network. The validation error and accuracy on Cat/Dog dataset were analyzed using activation functions (ReLU, Tanh, Selu, PRelu, Elu) at number of hidden layers. Overall the Relu gave best performance with the validation loss at 25th Epoch 0.3912 and validation accuracy at 25th Epoch 0.8320. Conclusion: It is found that a CNN model with ReLU hidden layers (3 hidden layers here) gives best results and improve overall performance better in term of accuracy and speed. These advantages of ReLU in CNN at number of hidden layers are helpful to effectively and fast retrieval of images from the databases.

scholarly journals Multichannel blind separation of sources algorithm based on cross-cumulant and the Levenberg-Marquardt method

1999 ◽  
Vol 47 (11) ◽  
pp. 3172-3175 ◽  
Author(s):  
A. Mansour ◽  
N. Ohnishi
Keyword(s):  
Blind Separation ◽  
Marquardt Method ◽  
Levenberg Marquardt
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