A new neural network model for solving random interval linear programming problems

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

“…Table 1 shows results of utilizing suggested model for fixed values w 1 = w 2 = 0.5 and some different values of 𝛼-cuts. We use the model in (19) to solve (44). Figure 8 depicts the transient behavior of x(t) of the model ( 19) with 4 random initial points and 𝛼 = 1, w 1 = w 2 = 1 2 .…”

“…Figure 8 depicts the transient behavior of x(t) of the model ( 19) with 4 random initial points and 𝛼 = 1, w 1 = w 2 = 1 2 . In fact, the Figure 8 shows the optimal solution of the problem (44) when the fuzziness goes to zero and choosing the parameter are w 1 = w 2 = 1 2 . Also, Figure 9 displays the transient behavior of x(t) by choosing the parameter 𝛼 = 0.2, w 1 = 0.3, w 2 = 0.7 with 8 random initial points.…”

“…There are several popular RNN's for constrained optimization which can be divided into the following two classes: First, the gradient based RNN's in References 30–41. Second, the projection type RNN's in References 42–59. Among gradient models, a globally convergent plan based on Fischer–Burmeister operators for solving second‐order cone constrained variational inequality problems by Nazemi and Sabeghi in Reference 32, by Nazemi and Mortezaee in Reference 33 for min‐max problems, for semidefinite programming problems by Nikseresht and Nazemi in Reference 36, by Miao et al 35 for efficiently solving nonlinear convex programs with second‐order cone constraints.…”

“…Among gradient models, a globally convergent plan based on Fischer–Burmeister operators for solving second‐order cone constrained variational inequality problems by Nazemi and Sabeghi in Reference 32, by Nazemi and Mortezaee in Reference 33 for min‐max problems, for semidefinite programming problems by Nikseresht and Nazemi in Reference 36, by Miao et al 35 for efficiently solving nonlinear convex programs with second‐order cone constraints. For projection models, Hu and Wang 42 for solving pseudo‐monotone variational inequalities and pseudo‐convex optimization problems examined a projection neural network, Arjmandzadeh et al 44 also studied a new neural network model for solving random interval linear programming problems, Effati et al 46 established a projection type neural network model to solve bilinear programming problems, He et al 50 introduced a projection type neural network model to solve variational inequalities, Mansoori et al 51 discussed on a model to solve the absolute value equations, Nikseresht and Nazemi 52,53 addressed two projection networks for linear and nonlinear semi‐definite programming problems, Karbasi et al 54,55 for fuzzy regression and Feizi and Nazemi 56 for stochastic support vector regression.…”