Implementation and analysis of training algorithms for the classification of infant cry with feed-forward neural networks

2004 ◽  
Author(s):  
J. Orozco ◽  
C.A. Reyes-Garcia
Keyword(s):  
Neural Networks ◽  
Training Algorithms ◽  
Feed Forward ◽  
Infant Cry ◽  
Feed Forward Neural Networks

scholarly journals On Training Of Feed Forward Neural Networks

2007 ◽  
Vol 4 (1) ◽  
pp. 158-164
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Training Algorithms ◽  
Back Propagation Algorithm ◽  
Performance Function ◽  
Feed Forward ◽  
Global Properties ◽  
Propagation Algorithm ◽  
Feed Forward Neural Networks ◽  
And Storage

In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

scholarly journals Algorithm for calculating synapse weights of the first layer of a neural network on the base of metric recognition methods. Part 1.

2020 ◽  
pp. 20-30
Author(s):  
Polad Geidarov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Three Dimensional ◽  
Analytical Calculation ◽  
Training Algorithms ◽  
Feed Forward Neural Network ◽  
Software Environment ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
And Training

Introduction: Metric recognition methods make it possible to preliminarily and strictly determine the structures of feed-forward neural networks, namely, the number of neurons, layers, and connections based on the initial parameters of the recognition problem. They also make it possible to analytically calculate the synapse weights of network neurons based on metric expressions. The setup procedure for these networks includes a sequential analytical calculation of the values of each synapse weight in the weight table for neurons of the zero or first layer, which allows us to obtain a working feed-forward neural network at the initial stage without the use of training algorithms. Then feed-forward neural networks can be trained by well-known learning algorithms, which generally speeds up the process of their creation and training. Purpose: To determine how much time the process of calculating the values of weights requires and, accordingly, how reasonable it is to preliminarily calculate the weights of a feed-forward neural network. Results: An algorithm is proposed and implemented for the automated calculation of all values of synapse weight tables for the zero and first layers as applied to the task of recognizing black-and-white monochrome symbol images. The proposed algorithm is described in the Builder C++ software environment. The possibility of optimizing the process of calculating the weights of synapses in order to accelerate the entire algorithm is considered. The time spent on calculating these weights for different configurations of neural networks based on metric recognition methods is estimated. Examples of creating and calculating synapse weight tables according to the considered algorithm are given. According to them, the analytical calculation of the weights of a neural network takes just seconds or minutes, being in no way comparable to the time necessary for training a neural network. Practical relevance: Analytical calculation of the weights of a neural network can significantly accelerate the process of creating and training a feed-forward neural network. Based on the proposed algorithm, we can implement one for calculating three-dimensional weight tables for more complex images, either blackand-white or color grayscale ones.

scholarly journals Algorithm for calculating synapse weights of the first layer of a neural network on the base of metric recognition methods. Part 2

2020 ◽  
pp. 25-38
Author(s):  
Polad Geidarov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Analytical Calculation ◽  
Training Algorithms ◽  
Feed Forward Neural Network ◽  
Software Environment ◽  
Feed Forward ◽  
Black And White ◽  
Feed Forward Neural Networks ◽  
And Training

Introduction: Metric recognition methods make it possible to preliminarily and strictly determine the structures of feed-forward neural networks, namely, the number of neurons, layers, and connections based on the initial parameters of the recognition problem. They also make it possible to analytically calculate the synapse weights of network neurons based on metric expressions. The setup procedure for these networks includes a sequential analytical calculation of the values of each synapse weight in the weight table for neurons of the zero or first layer, which allows us to obtain a working feed-forward neural network at the initial stage without the use of training algorithms. Then feed-forward neural networks can be trained by well-known learning algorithms, which generally speeds up the process of their creation and training. Purpose: To determine how much time the process of calculating the values of weights requires and, accordingly, how reasonable it is to preliminarily calculate the weights of a feed-forward neural network. Results: An algorithm is proposed and implemented for the automated calculation of all values of synapse weight tables for the zero and first layers as applied to the task of recognizing black-and-white monochrome symbol images. The proposed algorithm is described in the Builder C++ software environment. The possibility of optimizing the process of calculating the weights of synapses in order to accelerate the entire algorithm is considered. The time spent on calculating these weights for different configurations of neural networks based on metric recognition methods is estimated. Examples of creating and calculating synapse weight tables according to the considered algorithm are given. According to them, the analytical calculation of the weights of a neural network takes just seconds or minutes, being in no way comparable to the time necessary for training a neural network. Practical relevance: Analytical calculation of the weights of a neural network can significantly accelerate the process of creating and training a feed-forward neural network. Based on the proposed algorithm, we can implement one for calculating three-dimensional weight tables for more complex images, either black and-white or color grayscale ones.

JET CLASSIFICATION AT CDF

1992 ◽  
Vol 03 (supp01) ◽  
pp. 315-320
Author(s):  
S. Bianchin ◽  
A. Domini ◽  
M. Dall’Agata ◽  
M. De Nardi ◽  
F. Zara ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feed Forward ◽  
Model Dependence ◽  
Fisher Discriminant ◽  
First Results ◽  
Feed Forward Neural Networks

Results for the classification of jets obtained with the use feed-forward neural networks are reviewed with particular attention to model-dependence, flavor sensitivity and Et-dependence. First results obtained with a Kohonen-type network are also presented and both are compared with those obtained with a Fisher discriminant.

Sign in / Sign up

Export Citation Format

Share Document