Classifier systems for enhancing neural network-based global function approximations

1998 ◽  
Author(s):  
P. Hajela ◽  
B. Kim
Keyword(s):  
Neural Network ◽  
Classifier Systems ◽  
Global Function ◽  
Function Approximations

scholarly journals Global Optimization Method to Multiple Local Optimals with the Surface Approximation Methodology and Its Application for Industry Problems

2021 ◽  
Author(s):  
Ichiro Hagiwara
Keyword(s):  
Neural Network ◽  
Region Of Interest ◽  
Optimization Method ◽  
Mapping Method ◽  
Vehicle Performance ◽  
Vehicle Component ◽  
Generalized Inverse Matrix ◽  
Global Optimal ◽  
Function Approximations ◽  
Vehicle Crashworthiness

Although generally speaking, a great number of functional evaluations may be required until convergence, it can be solved by using neural network effectively. Here, techniques to search the region of interest containing the global optimal design selected by random seeds is investigated. Also techniques for finding more accurate approximation using Holographic Neural Network (HNN) improved by using penalty function for generalized inverse matrix is investigated. Furthermore, the mapping method of extrapolation is proposed to make the technique available to general application in structural optimization. Application examples show that HNN may be expected as potential activate and feasible surface functions in response surface methodology than the polynomials in function approximations. Finally, the real design examples of a vehicle performance such as idling vibration, booming noise, vehicle component crash worthiness and combination problem between vehicle crashworthiness and restraint device performance at the head-on collision are used to show the effectiveness of the proposed method.

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.

Adaptive Optical Neural Network Classifier Systems

1997 ◽  
Author(s):  
Wesley E. Foor ◽  
Mark A. Getbehead ◽  
James B. Rosetti
Keyword(s):  
Neural Network ◽  
Neural Network Classifier ◽  
Classifier Systems ◽  
Optical Neural Network
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