a neural network model to solve dea problems
Authors
abstract
the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.
similar resources
A Neural Network Model to Solve DEA Problems
The paper deals with Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN). We believe that solving for the DEA efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. In this paper, a new neural network model is used to estimate the inefficiency of DMUs in large datasets.
full textCompetitive neural network to solve scheduling problems
Most scheduling problems have been demonstrated to be NP-complete problems. The Hop"eld neural network is commonly applied to obtain an optimal solution in various di!erent scheduling applications, such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Hop"eld neural networks, although providing rapid convergence to the solution, require extensive e!ort to deter...
full textUsing MOLP based procedures to solve DEA problems
Data envelopment analysis (DEA) is a technique used to evaluate the relative efficiency of comparable decision making units (DMUs) with multiple input-output. It computes a scalar measure of efficiency and discriminates between efficient and inefficient DMUs. It can also provide reference units for inefficient DMUs without consideration of the decision makers’ (DMs) preferences. In this paper, ...
full textAPPLICATION NEURAL NETWORK TO SOLVE ORDINARY DIFFERENTIAL EQUATIONS
In this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. In proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. In comparison with existing similar neural networks proposed model provides solutions with high accuracy. Numerica...
full textUsing Duo Output Neural Network to Solve Binary Classification Problems
This paper proposes an approach to solve binary classification problems using Duo Output Neural Network (DONN). DONN is a neural network trained to predict a pair of complementary outputs which are the truth and falsity values. In this paper, outputs obtained from two DONNs are aggregated and used to predict the classification result. The first DONN is trained to predict a pair of truth and fal...
full textUsing Neural Network Formalism to Solve Multiple-Instance Problems
Many objects in the real world are difficult to describe by means of a single numerical vector of a fixed length, whereas describing them by means of a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining in importance throughout the last years. MIL formalism assumes that each object (sample) is represented by a set (bag) of feature...
full textMy Resources
Save resource for easier access later
Journal title:
international journal of data envelopment analysisISSN 2345-458X
volume 2
issue 3 2014
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023