Exact(7)
In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated.
In Jmol, these variable substitutions are carried out as preprocessing steps, in a purely lexical fashion.
In order to transform this optimization problem into a convex form, we perform the variable substitutions (26).
Using these variable substitutions, and the equivalent constraints in (14), the optimization problem in (25) can be reformulated as (27).
Then, the following integrals from pervious expression can be presented in the form of (A.2)Now, they can easily be solved using variable substitutions: (A.3 and well-known definition of Gamma function:.
Then, the following integrals from the pervious expression can be presented in the form: (A.3)Now, they can easily be solved using variable substitutions (A.4 and well-known definition of Gamma function:.
Similar(53)
Using the variable substitution method, state space differential equations of the structure are established.
A variable substitution method, in which the main branch of logarithmic function is chosen, is proposed to solve this problem.
The solution methods include variable substitution, perturbation technique, Laplace transformation, Sturm-Liouville eigenvalue theory, orthogonal transformation and numerical inversion.
A novel static output-feedback design method based on the variable substitution is employed in the controller design.
Firstly, by constructing a proper variable substitution,the original system is transformed into the first order differential system.
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