Economic Experiments with Swarm: A Neural Network Approach to the Self-Development of Consistency in Agents’ Behavior

2000 ◽  
pp. 73-103 ◽  
Author(s):  
Pietro Terna
Keyword(s):  
Neural Network ◽  
The Self ◽  
Network Approach ◽  
Neural Network Approach ◽  
Economic Experiments ◽  
Self Development

The Influence Function of Principal Component Analysis by Self-Organizing Rule

1998 ◽  
Vol 10 (6) ◽  
pp. 1435-1444 ◽  
Author(s):  
Isao Higuchi ◽  
Shinto Eguchi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Influence Function ◽  
Principal Component ◽  
Component Analysis ◽  
The Self ◽  
Network Approach ◽  
Neural Network Approach ◽  
Principal Component Vector ◽  
Self Organizing

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

The psychopharmacology of latent inhibition: A neural network approach

1997 ◽  
Author(s):  
Nestor A. Schmajuk ◽  
Catalin V. Buhusi ◽  
Jeffrey A. Gray
Keyword(s):  
Neural Network ◽  
Latent Inhibition ◽  
Network Approach ◽  
Neural Network Approach

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach

A Neural Network Approach In A Backward Heat Conduction Problem

2016 ◽  
Author(s):  
Fabio Tokio Mikki ◽  
Edison Issamoto ◽  
Jefferson I. da Luz ◽  
Pedro Paulo Balbi de Oliveira ◽  
Haroldo F. Campos-Velho ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Network Approach ◽  
Neural Network Approach
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