linear programming problem

In this paper, we propose a new hybrid neural network including Data Envelopment Analysis(DEA) and Radial Basis Function Network (RBFN)for binary classification problems. In the supervised learning phase of the neural network, the additive model is used to learn the classification function and Gaussian Radial Basis Function (GRBF) is used in the unsupervised learning phase of the neural network. Compared with the existing RBFN-DEA model for solving classification problems, the proposed model has low CPU time and can be applied to solve classification problems with negative data.

Generally, linear programming (LP) problem is the most extensively utilized technique for solving and optimizing real-world problems due to its simplicity and efficiency. However, to deal with the inaccurate data, the neutrosophic set theory comes into play, which creates a simulation of the human decision-making process by considering all parts of the choice (i.e., agree, not sure, and disagree). Keeping the bene ts in mind, we proposed the neutrosophic LP models based on triangular neutrosophic numbers (TNN) and provided a method for solving them. Fuzzy LP problem can be converted into crips LP problem based on the de ned ranking function. The provided technique has been demonstrated with numerical examples given by Abdelfattah. Finally, we found that, when compared to previous approaches, the suggested method is simpler, more efficient, and capable of solving all types of fuzzy LP models.