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A hybrid learning scheme combining EM and MASMOD algorithms for fuzzy local linearization modeling.

Q Gan¹, C J Harris

¹Image, Speech and Intelligent Systems Research Group, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. qg@ecs.soton.ac.uk

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

This study introduces a hybrid learning scheme for Fuzzy Local Linearization (FLL) modeling. The method enhances nonlinear system modeling for state estimation and control by combining adaptive splines and expectation-maximization algorithms.

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Area of Science:

Control Engineering
Machine Learning
System Identification

Background:

Fuzzy Local Linearization (FLL) is a divide-and-conquer approach for complex nonlinear system modeling.
Accurate modeling is crucial for state estimation and control applications.

Purpose of the Study:

To propose a novel hybrid learning scheme for Fuzzy Local Linearization (FLL) modeling.
To improve the approximation ability, network parsimony, and error covariance estimation in FLL models.

Main Methods:

A hybrid learning scheme combining Modified Adaptive Spline Modeling (MASMOD) for antecedent parts (membership functions).
Expectation-Maximization (EM) algorithm for parameterizing consequent parts (local linear models).

Main Results:

The hybrid FLL model demonstrates approximation capabilities comparable to neuro-fuzzy networks.
Achieved a parsimonious network structure and provided valuable model error covariance information.

Conclusions:

The proposed hybrid learning scheme effectively models unknown nonlinear systems.
The method is suitable for applications requiring accurate state estimation and control with uncertainty quantification.