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A neural network approach-decision neural network (DNN) for preference assessment
By: Song Lin; Jian Chen;
2004 / IEEE
Description
This item was taken from the IEEE Periodical ' A neural network approach-decision neural network (DNN) for preference assessment ' A new neural-network-based approach to assess the preference of a decision-maker (DM) for the multiple objective decision making (MODM) problem is presented in this paper. A new neural network structure with a ""twin-topology"" is introduced in this approach. We call this neural network a decision neural network (DNN). The characteristics of the DNN are discussed, and the training algorithm for DNN is presented as well. The DNN enables the decision-maker to make pairwise comparisons between different alternatives, and these comparison results are used as learning samples to train the DNN. The DNN is applicable for both accurate and inaccurate comparisons (results are given in approximate values or interval scales). The performance of the DNN is evaluated with several typical forms of utility functions. Results show that DNN is an effective and efficient way for modeling the preference of a decision-maker.
Related Topics
Decision Making
Learning (artificial Intelligence)
Utility Theory
Neural Nets
Preference Assessment
Multiple Objective Decision Making Problem
Training Algorithm
Utility Functions
Neural Networks
Decision Making
Utility Theory
Artificial Neural Networks
Delta Modulation
Economic Forecasting
Pattern Recognition
Neurons
Topology
Signal Processing And Analysis
Engineering
Decision Neural Network