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논문검색

Genetic Algorithm-based Fuzzy Optimization Neural Network Model for WSNs Performance Evaluation

초록

영어

In recent years, wireless sensor network is becoming the hottest research fields, which opens up a wealth of opportunities for many application domains. Comprehensive performance of WSNs is important for planning and management of a construction network. In order to deal with the deficiency of existing evaluation methods for WSNs performance, an intelligent evaluation model based on fuzzy optimization neural network model is proposed, which introduces genetic algorithm to optimize the connection weight of the neural network model to achieve approximate optimal solution. The weights are to be regarded as initial values for the next step that a neural network is tuned finely further. Fuzzy optimization model is as activation function of the neural network, therefore the model has explicit physical meanings. This model is applied to evaluate WSNs performance of some samples, and the comparative analysis with other models shows that the model improves evaluation precision and efficiency and is practical. The first part is the research status and related problems. The second part is the establishment of evaluation model. The last part is the experimental analysis and conclusion.

목차

Abstract
 1. Introduction
 2. Fuzzy Optimization Neural Network Model
  2.1. Fuzzy Optimization Model
  2.2. Neural Network Model
 3. Improved Fuzzy Optimization Neural Network Model based on Genetic Algorithm
  3.1. To Determine the Encoding Method
  3.2. The Population Initialization
  3.3. Fitness calculation and set other parameters of GA
  3.4 The process of evaluation model
 4. A Numerical Example
  4.1. Select the Input and Output Parameters
  4.2. Input and Output Data Preprocessing
  4.3. Analysis of the results
 5. Conclusion
 Acknowledgements
 References

저자정보

  • Fawang Han Nanjing Forest Police College, Jiangsu Nanjing 210023, China

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