Accurate model reduction of large scale systems using adaptive multi-objective particle swarm optimization algorithm

Kazemi, Amin; Behinfaraz, Reza; Ghiasi, Amir

doi:10.1109/icmsc.2017.7959504

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Many well‐known optimizing method has been introduced . Consider the controllers u i as Equation and Risk i as Equation for i = 1,2,3.…”

Section: Reliability and Risk Analysis Of Proposed Controllermentioning

confidence: 99%

“…Now, object is to minimize the mean of risk of each controllers in 10 seconds. Then, cost function is defined as

f = m e a n {false(R i s k_{1} false)}^{2} + m e a n {false(R i s k_{2} false)}^{2} + m e a n {false(R i s k_{3} false)}^{2} .

For minimizing the risk, we use particle swarm optimization (PSO) algorithm to determine the controller coefficients k 1 , k 2 , and k 3 subject to stability conditions for Equation .…”

Section: Reliability and Risk Analysis Of Proposed Controllermentioning

confidence: 99%

“…Many well-known optimizing method has been introduced. 40,41 Consider the controllers u i as Equation 15 and Risk i as Equation 33 for i = 1, 2, 3. Now, object is to minimize the mean of risk of each controllers in 10 seconds.…”

Section: Reduction Of Controller Riskmentioning

confidence: 99%

“…For minimizing the risk, we use particle swarm optimization (PSO) algorithm 41 to determine the controller coefficients k 1 , k 2 , and k 3 subject to stability conditions for Equation 16.…”

Section: Reduction Of Controller Riskmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive synchronization of new fractional‐order chaotic systems with fractional adaption laws based on risk analysis

Behinfaraz

Ghaemi

Khanmohammadi

2019

Math Methods in App Sciences

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In this paper, a new fractional‐order chaotic system and an adaptive synchronization of fractional‐order chaotic system are proposed. Parameters adaption laws are obtained to design adaptive controllers using Lyapunov stability theory of fractional‐order system. Finally, reliability of designed controllers and risk analysis of adaptive synchronization problem are formulated and, risk of using the proposed controllers in presences of external disturbances are demonstrated. Also, risk of controllers are reduced using an optimizing method. Numerical examples are used to verify the performance of the proposed controllers.

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“…Many well‐known optimizing method has been introduced . Consider the controllers u i as Equation and Risk i as Equation for i = 1,2,3.…”

Section: Reliability and Risk Analysis Of Proposed Controllermentioning

confidence: 99%

“…Now, object is to minimize the mean of risk of each controllers in 10 seconds. Then, cost function is defined as

f = m e a n {false(R i s k_{1} false)}^{2} + m e a n {false(R i s k_{2} false)}^{2} + m e a n {false(R i s k_{3} false)}^{2} .

For minimizing the risk, we use particle swarm optimization (PSO) algorithm to determine the controller coefficients k 1 , k 2 , and k 3 subject to stability conditions for Equation .…”

Section: Reliability and Risk Analysis Of Proposed Controllermentioning

confidence: 99%