Narrow Results

The convergence theory is a basic theory of topology and analysis. In this paper, we establish the Moore-Smith semi-pre-convergence theory of molecular nets in fuzzy lattices and then discuss its applications.

In this work, a new solution for the bipolar-fuzzy initial value problem is proposed. This method is obtained by approximating the solution by piece-wise polynomial of order three. The existence and uniqueness of the solution and convergence of the method are discussed in detail. Finally, for more explanation, several numerical examples are solved.

In this paper, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is given using a projection recurrent neural network (RNN) scheme. Since there is a few research for resolving of FNLOP by RNNs, we describe a new framework to solve the problem. By reformulating the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are obtained. Moreover, we utilize the KKT conditions into a RNN as a capable tool to solve the problem. Besides, the global convergence and the Lyapunov stability of the neuro-dynamic model are established. In the final step, some simulation examples are stated to validate the obtained results. Reported results are compared with some other previous neural networks.