Improved CPN Network Applying in the Failure Diagnosis of Electronic Gasoline

Jing Kong; Su Su Bao

doi:10.4028/www.scientific.net/AMR.179-180.60

Paper Titles

Numerical Simulation for the System of Ordinary Differential Equations
p.37

Robust Reliability Design for Networked Control Systems Based on Information Scheduling
p.43

Materials Formation Mechanism and Experimental Verification of Micron Long Fiber
p.49

A New Algorithm of Association Rules Mining Based on Relation Matrix
p.55

Improved CPN Network Applying in the Failure Diagnosis of Electronic Gasoline
p.60

High Accuracy Computational Method for Ordinary Differential Equation with Two Small Parameters
p.64

Effect of Warm-up Temperature Rates on Morphologies of Porous Polyimide Membranes
p.70

Study of Deformation Behavior of Ultra-High Molecular Weight Polyethylene (UHMWPE)
p.75

Rigid Body Spring Model for Large Aggregate Concrete
p.79

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 179-180Improved CPN Network Applying in the Failure...

Improved CPN Network Applying in the Failure Diagnosis of Electronic Gasoline

Abstract:

We diagnose the Electronic Gasoline failure according to the improved CPN neural network. In the normal CPN neural network, it allows one neuron to win. We improved the CPN neural network based on the developed CPN and allow two neurons to win in the competition layer. The two winning neurons affect the Weights simultaneously. This improved method can adjust the weight values in the CPN model more accurately and give more accurate output for testing data.