Application of the Transiently Chaotic Neural Network to Nonlinear Constraint Optimization Problems

2006 ◽  
Author(s):  
Xinyu Li ◽  
Dongyi Chen
Keyword(s):  
Neural Network ◽  
Optimization Problems ◽  
Constraint Optimization ◽  
Chaotic Neural Network ◽  
Nonlinear Constraint ◽  
Constraint Optimization Problems

scholarly journals Minimization of the Settling Time of Variable Area Flowmeters

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 530 ◽  
Author(s):  
Mateusz Turkowski ◽  
Artur Szczecki ◽  
Maciej Szudarek
Keyword(s):  
Optimization Problems ◽  
Dynamic Performance ◽  
Transient Behavior ◽  
Simplex Algorithm ◽  
Settling Time ◽  
Constraint Optimization ◽  
Nonlinear Constraint ◽  
First Case ◽  
Constraint Optimization Problems ◽  
Tube Geometry

In the article a differential equation describing transient behavior of variable area (VA) meters has been developed and validated experimentally for air as a measured fluid and for two float shapes—plumb bob and sphere. A modified version of simplex algorithm adapted for nonlinear constraint optimization problems was applied to minimize the settling time of VA meters in two cases. In the first case both the float and tube geometry were altered. In the second case only the float geometry was modified. The second case has been validated experimentally. The theory and experiment is in reasonable agreement (under 5% of full scale), which is satisfactory for the purposes of optimization of VA flowmeters dynamic performance. Analytical model of VA flowmeter has been proven to be a proper tool for optimization. Settling times obtained during the optimization process were several times shorter than these of commercially manufactured instruments. Overshoot has not exceeded the assumed value of 3%.

scholarly journals Beyond Trees: Analysis and Convergence of Belief Propagation in Graphs with Multiple Cycles

2020 ◽  
Vol 34 (05) ◽  
pp. 7333-7340
Author(s):  
Roie Zivan ◽  
Omer Lev ◽  
Rotem Galiki
Keyword(s):  
Graphical Models ◽  
Belief Propagation ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Damping Factor ◽  
Constraint Optimization ◽  
Single Cycle ◽  
Distributed Constraint Optimization ◽  
Multiple Cycles ◽  
Constraint Optimization Problems

Belief propagation, an algorithm for solving problems represented by graphical models, has long been known to converge to the optimal solution when the graph is a tree. When the graph representing the problem includes a single cycle, the algorithm either converges to the optimal solution or performs periodic oscillations. While the conditions that trigger these two behaviors have been established, the question regarding the convergence and divergence of the algorithm on graphs that include more than one cycle is still open.Focusing on Max-sum, the version of belief propagation for solving distributed constraint optimization problems (DCOPs), we extend the theory on the behavior of belief propagation in general – and Max-sum specifically – when solving problems represented by graphs with multiple cycles. This includes: 1) Generalizing the results obtained for graphs with a single cycle to graphs with multiple cycles, by using backtrack cost trees (BCT). 2) Proving that when the algorithm is applied to adjacent symmetric cycles, the use of a large enough damping factor guarantees convergence to the optimal solution.

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