Political Optimizer Based Feedforward Neural Network for Classification and Function Approximation

2021 ◽  
Author(s):  
Qamar Askari ◽  
Irfan Younas
Keyword(s):  
Neural Network ◽  
Function Approximation ◽  
Feedforward Neural Network ◽  
And Function

A FEEDFORWARD NEURAL NETWORK CLASSIFIER MODEL: MULTIPLE CLASSES, CONFIDENCE OUTPUT VALUES, AND IMPLEMENTATION

1992 ◽  
Vol 06 (04) ◽  
pp. 539-569
Author(s):  
CRIS KOUTSOUGERAS ◽  
GEORGE GEORGIOU ◽  
CHRISTOS PAPACHRISTOU
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Adaptive Algorithm ◽  
Difficult Problem ◽  
Feedforward Neural Network ◽  
Neural Network Classifier ◽  
Optical Processing ◽  
Internal Representations ◽  
Processing Elements ◽  
And Function

The Athena model is a tree-like net for pattern classification. This paper presents the formalisms on which the model's internal representations and function are based. It also presents an adaptive algorithm to be used with this model. The adaptation is based on entropy optimization. The difficult problem of the optimization is handled by use of Fisher's multiple discriminants method. A method is also presented by which confidence values are produced for the overall classification decision. Finally, a data flow architecture using optical processing elements is considered for the model's implementation.

scholarly journals Comparison of Feedforward and Recurrent Neural Network in Forecasting Chaotic Dynamical System

2019 ◽  
Vol 10 (37) ◽  
pp. 31-44
Author(s):  
Engin Kandıran ◽  
Avadis Hacınlıyan
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Time Series ◽  
Function Approximation ◽  
Feedforward Neural Network ◽  
Network Architectures ◽  
Chaotic Dynamical System ◽  
Global Function ◽  
The Neural Network ◽  
Hidden Layer

Artificial neural networks are commonly accepted as a very successful tool for global function approximation. Because of this reason, they are considered as a good approach to forecasting chaotic time series in many studies. For a given time series, the Lyapunov exponent is a good parameter to characterize the series as chaotic or not. In this study, we use three different neural network architectures to test capabilities of the neural network in forecasting time series generated from different dynamical systems. In addition to forecasting time series, using the feedforward neural network with single hidden layer, Lyapunov exponents of the studied systems are forecasted.

Economic Analysis of Risky Projects Based on LS-SVM

2011 ◽  
Vol 63-64 ◽  
pp. 403-406
Author(s):  
Xin Chen Guo ◽  
Chao Zhang ◽  
Fu Wei Zhang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Regression Analysis ◽  
Function Approximation ◽  
Prediction Accuracy ◽  
Analytical Models ◽  
Accurate Analysis ◽  
Mlp Neural Network ◽  
And Function ◽  
Risky Projects

Risk management and its accurate analysis are very important for project management. RBF and MLP Neural Network Model are common methods of risk management and analysis, which are not accurate enough. In this paper a new method based on LS-SVM is introduced. Analytical models of risky projects are investigated and function approximation results are compared. Experimental results show that the regression analysis of risk based on LS-SVM method has higher prediction accuracy and better generalization ability.

scholarly journals Analysis of Multi layer Perceptron Network

2013 ◽  
Vol 7 (2) ◽  
pp. 600-606
Author(s):  
Jatinder Kaur ◽  
Dr. Mandeep Singh ◽  
Pardeep Singh Bains ◽  
Gagandeep Singh
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Multilayer Perceptron ◽  
Function Approximation ◽  
Back Propagation ◽  
Feedforward Neural Network ◽  
Mean Square ◽  
Multi Layer Perceptron ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm

In this paper, we introduce the multilayer Perceptron (feedforward) neural network (MLPs) and used it for a function approximation. For the training of MLP, we have used back propagation algorithm principle. The main purpose of this paper lies in changing the number of hidden layers of MLP for achieving minimum value of mean square error.

Sign in / Sign up

Export Citation Format

Share Document