Sensitivity Analysis of a Feedforward Neural Network for Considering Genetic Mechanisms of Kuroko Deposits

2003 ◽  
Vol 12 (4) ◽  
pp. 291-301 ◽  
Author(s):  
Setsuro Matsuda ◽  
Katsuaki Koike
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Feedforward Neural Network ◽  
Genetic Mechanisms ◽  
Kuroko Deposits

scholarly journals Simulation of Seawater Intrusion Area Using Feedforward Neural Network in Longkou, China

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2107
Author(s):  
Daiyuan Li ◽  
Yongxiang Wu ◽  
Erkun Gao ◽  
Gaoxu Wang ◽  
Yi Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Seawater Intrusion ◽  
Groundwater Resources ◽  
Absolute Error ◽  
Feedforward Neural Network ◽  
Impact Factors ◽  
Efficiency Coefficient ◽  
The Mean ◽  
The Relationship

Reliable simulation of seawater intrusion (SI) is necessary for sustainable groundwater utilization. As a powerful tool, feedforward neural network (FNN) was applied to study seawater intrusion area (SIA) fluctuations in Longkou, China. In the present study, changes of groundwater level (GWL) were modeled by FNN Model 1. Then, FNN Model 2 was developed for fitting the relationship between GWL and SIA. Finally, two models were integrated to simulate SIA changes in response to climatic and artificial factors. The sensitivity analysis of each impact factor was conducted by the “stepwise” method to quantify the relative importance for SIA and GWL. The results from the integrated model indicated that this method could accurately reproduce SIA fluctuations when the Nash–Sutcliffe efficiency coefficient was 0.964, the root mean square error was 1.052 km2, the correlation coefficient was 0.983, and the mean absolute error was 0.782 km2. The results of sensitivity analysis prove that precipitation and groundwater pumping for agriculture mainly affect fluctuations of SIA in the study area. It can be concluded that FNN is effectively used for modeling SI fluctuations together with GWL, which can provide enough support for the sustainable management of groundwater resources with consideration of crucial impact factors of seawater intrusion (SI).

A MEASURE OF RELATIVE ROBUSTNESS FOR FEEDFORWARD NEURAL NETWORKS SUBJECT TO SMALL INPUT PERTURBATIONS

1992 ◽  
Vol 03 (03) ◽  
pp. 291-299 ◽  
Author(s):  
MO-YUEN CHOW ◽  
SUE OI YEE
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sensitivity Analysis ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Network Robustness ◽  
Input Output ◽  
Robustness Measure ◽  
On Line ◽  
Relative Robustness

The relative robustness of artificial neural networks subject to small input perturbations (e.g. measurement noises) is an important issue in real world applications. This paper uses the concept of input-output sensitivity analysis to derive a relative network robustness measure for different feedforward neural network configurations. For illustration purposes, this measure is used to compare different neural network configurations designed for detecting incipient faults in induction motors. Analytical and simulation results are presented to show that the relative network robustness measure derived in this paper is an effective indicator of the relative performance of different feedforward neural network configurations in noisy environments and that this measure should be considered in the design of neural networks for real time applications. The concept of input-output sensitivity analysis and relative network robustness measure presented can be extended to analyze other neural networks designed for on-line applications.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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