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Cumulant-based parameter estimation using structured networks
1Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA.
IEEE Transactions on Neural Networks
|January 1, 1991
Summary
A novel structured network estimates moving-average model parameters using cumulant matching. This method utilizes a feedforward network with memory units to learn statistical patterns for accurate parameter estimation.
Area of Science:
- Signal Processing
- Machine Learning
- Statistical Modeling
Background:
- Moving-average (MA) models are fundamental in time series analysis.
- Estimating MA model parameters accurately is crucial for various applications.
- Cumulant matching offers a robust approach for parameter estimation.
Purpose of the Study:
- To develop a novel structured network for estimating moving-average model parameters.
- To leverage second-order and third-order cumulant matching for parameter estimation.
- To provide a network with interpretable weights representing MA parameters.
Main Methods:
- A two-level, three-layer structured feedforward network is proposed.
- The network employs random access memory units to control summer connectivities.
- A steepest-descent algorithm is used for training the network to learn cumulant patterns.
Main Results:
- The structured network's weights directly correspond to the moving-average parameters.
- Network outputs match desired second-order or third-order statistics when weights are accurate.
- Simulations demonstrate the effectiveness of the proposed estimation method.
Conclusions:
- The developed structured network provides an effective method for estimating moving-average model parameters.
- The network's architecture offers physical interpretability of estimated parameters.
- The cumulant matching approach combined with the structured network shows promise for advanced signal processing tasks.
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