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A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks
1Department of Industrial Engineering and Engineering Management, Integration and Collaboration Laboratory, National Tsing Hua University, Hsinchu, Taiwan.
IEEE Transactions on Neural Networks and Learning Systems
|August 24, 2016
Summary
The novel squeezed artificial neural network (SqANN) method improves network reliability calculations by using Monte Carlo simulation and Taguchi methods. This approach offers superior accuracy over traditional methods for symbolic network reliability functions (SNRFs).
Area of Science:
- Computer Science
- Network Engineering
- Artificial Intelligence
Background:
- Network reliability is crucial for decision support, requiring accurate calculation of symbolic network reliability functions (SNRFs).
- Dynamic changes in network parameters necessitate efficient methods for evaluating SNRFs.
- Traditional artificial neural network (ANN) approaches may have limitations in accuracy and efficiency for SNRF evaluation.
Purpose of the Study:
- To propose and evaluate a novel squeezed artificial neural network (SqANN) approach for estimating SNRFs.
- To compare the performance of the SqANN method against traditional ANN-based approaches.
- To optimize ANN parameters for SNRF evaluation using the Taguchi method.
Main Methods:
- Utilized Monte Carlo simulation to estimate network reliability based on Box-Behnken design matrices.
- Implemented the Taguchi method to determine optimal hyperparameters (neurons, activation functions) for the ANN.
- Developed and applied the squeezed artificial neural network (SqANN) for SNRF evaluation.
Main Results:
- The SqANN method demonstrated superior performance compared to traditional ANN-based approaches.
- Achieved at least a 16.6% improvement in median absolute deviation.
- The enhanced accuracy came with a minimal average time cost of approximately 2 seconds across experiments.
Conclusions:
- The proposed SqANN approach offers a more accurate and efficient method for evaluating symbolic network reliability functions.
- The integration of Monte Carlo simulation and Taguchi methods provides an effective strategy for optimizing neural network performance in reliability analysis.
- SqANN represents a significant advancement for network reliability assessment in dynamic environments.
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