On-Line Node Fault Injection Training Algorithm for MLP Networks: Objective Function and Convergence Analysis

2012 ◽  
Vol 23 (2) ◽  
pp. 211-222 ◽  
Author(s):  
J. P. Sum ◽  
Chi-Sing Leung ◽  
K. I-J Ho
Keyword(s):  
Objective Function ◽  
Convergence Analysis ◽  
Fault Injection ◽  
Training Algorithm ◽  
On Line ◽  
Line Node

scholarly journals Recursive Neural Networks Based on PSO for Image Parsing

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
State Of The Art ◽  
Pso Algorithm ◽  
Training Algorithm ◽  
Image Parsing ◽  
Swarm Optimization ◽  
Recursive Neural Networks ◽  
Parsing Algorithm

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

A Digital On-Line Monitor for Detecting Intermittent Resistance Faults at Board Level

2019 ◽  
Vol 28 (supp01) ◽  
pp. 1940003
Author(s):  
Hassan Ebrahimi ◽  
Hans G. Kerkhoff
Keyword(s):  
Fault Injection ◽  
Occurrence Rate ◽  
Time Duration ◽  
Management Framework ◽  
Level Data ◽  
Random Behavior ◽  
On Line ◽  
Serial Peripheral Interface ◽  
Board Level

The reliability of board-level data communications intensively depends on the reliability of interconnections on a board. One of the most challenging interconnections reliability threats is intermittent resistive faults (IRFs). Detecting such faults is a major challenge. The main reason is the random behavior of these faults. They may occur randomly in time, duration and amplitude. The occurrence rate can vary from a few nanoseconds to months. This paper investigates IRF detection at the board level by introducing a new digital in situ IRF monitor. Hardware-based fault injection has been used to validate the proposed IRF monitor. As case studies, two widely used on-board transmission protocols namely the Universal Asynchronous Receiver Transmitter (UART) and the Serial Peripheral Interface bus (SPI), have been used. In addition, one fault management framework, based on the IJTAG standard, has been implemented to collect and characterize information from the monitors. The experimental results show that the proposed monitor is effective in detecting IRFs at the board level.

Mechanism Simulation Based on ADAMS and Structure Optimization for Automobile Punching Parts On-Line Detector

2011 ◽  
Vol 211-212 ◽  
pp. 619-623
Author(s):  
Xi Xin Rao ◽  
Kang He ◽  
He Sheng Liu
Keyword(s):  
Objective Function ◽  
Dynamic Performance ◽  
Mathematic Model ◽  
Design Parameters ◽  
Work Piece ◽  
Simulation And Optimization ◽  
Design Variables ◽  
Simulation Based ◽  
On Line ◽  
Line Detector

Camera Device is crucial components of Automobile punching parts on-line detector and Its dynamic characteristics has a critical influence on the accuracy of Automobile punching parts on-line detector. To reduce the relative acceleration of Camera Device to the measured part, biaxial body of Automobile Punching Parts On-line Detector was optimized. On the basis of analyzing mechanism, simplifying the prototype, determining the design variables and the objective function and the constraint condition, this paper puts forward the parameter optimization mathematic model with the minimum of the acceleration of Camera Device relative to the point on the measured work piece as objective function and completes mechanism simulation and optimization by the ADAMS software. The results show that some design parameters gets more reasonable and dynamic performance of Automobile punching parts on-line detector is better.

scholarly journals Convergence Analysis of the Hessian Estimation Evolution Strategy

2021 ◽  
pp. 1-25
Author(s):  
Tobias Glasmachers ◽  
Oswin Krause
Keyword(s):  
Objective Function ◽  
Convergence Analysis ◽  
Covariance Matrix ◽  
Linear Convergence ◽  
Evolution Strategy ◽  
Sampling Distribution ◽  
Evolution Strategies ◽  
Problem Instance ◽  
Family Stability ◽  
The Family

Abstract The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this paper we formally prove two strong guarantees for the (1+4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance.

A note on the accelerated proximal gradient method for nonconvex optimization

2018 ◽  
Vol 34 (3) ◽  
pp. 449-457
Author(s):  
HUIJUAN WANG ◽  
◽  
HONG-KUN XU ◽  
Keyword(s):  
Machine Learning ◽  
Objective Function ◽  
Convergence Analysis ◽  
Nonconvex Optimization ◽  
Gradient Method ◽  
Optimization Problem ◽  
Proximal Gradient Method ◽  
International Conference ◽  
Variable Stepsizes ◽  
Sydney Australia

We improve a recent accelerated proximal gradient (APG) method in [Li, Q., Zhou, Y., Liang, Y. and Varshney, P. K., Convergence analysis of proximal gradient with momentum for nonconvex optimization, in Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017] for nonconvex optimization by allowing variable stepsizes. We prove the convergence of the APG method for a composite nonconvex optimization problem under the assumption that the composite objective function satisfies the Kurdyka-Łojasiewicz property.

scholarly journals Statistical Mechanics of On-line Node-perturbation Learning

2011 ◽  
Vol 4 ◽  
pp. 23-32 ◽  
Author(s):  
Kazuyuki Hara ◽  
Kentaro Katahira ◽  
Kazuo Okanoya ◽  
Masato Okada
Keyword(s):  
Statistical Mechanics ◽  
On Line ◽  
Line Node

A computation-efficient on-line training algorithm for neurofuzzy networks

2000 ◽  
Vol 31 (3) ◽  
pp. 297-306 ◽  
Author(s):  
C. W. Chan ◽  
K. C. Cheung ◽  
W. K. Yeung
Keyword(s):  
Training Algorithm ◽  
On Line ◽  
Neurofuzzy Networks
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