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    A Nonlinear System Identification based on Additive Expression Tree Model with Cuckoo Search

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    https://www.riss.kr/link?id=A100956301

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    In this paper, an efficient approach of combining additive expression tree model (AET) with hybrid evolutionary method is proposed to identify nonlinear systems. As linear variant of additive tree model, additive expression tree model is proposed to encode the mathematical formulations. For finding the optimal structure and parameters of systems, a hybrid evolutionary method integrating a new structure based evolutionary algorithm and cuckoo search is employed. We illustrate some experimental comparisons with neural network, neural network integrating fuzzy system and symbolic regression methods. Experimental results reveal that our model and optimization method perform better.
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    In this paper, an efficient approach of combining additive expression tree model (AET) with hybrid evolutionary method is proposed to identify nonlinear systems. As linear variant of additive tree model, additive expression tree model is proposed to e...

    In this paper, an efficient approach of combining additive expression tree model (AET) with hybrid evolutionary method is proposed to identify nonlinear systems. As linear variant of additive tree model, additive expression tree model is proposed to encode the mathematical formulations. For finding the optimal structure and parameters of systems, a hybrid evolutionary method integrating a new structure based evolutionary algorithm and cuckoo search is employed. We illustrate some experimental comparisons with neural network, neural network integrating fuzzy system and symbolic regression methods. Experimental results reveal that our model and optimization method perform better.

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    목차 (Table of Contents)

    • Abstract
    • 1. Introduction
    • 2. Materials and Methods
    • 2.1. Additive Expression Tree Model
    • 2.2. Structure Optimization Methods
    • Abstract
    • 1. Introduction
    • 2. Materials and Methods
    • 2.1. Additive Expression Tree Model
    • 2.2. Structure Optimization Methods
    • 2.3. Parameter Optimization of Models using Cuckoo Search
    • 2.4. Fitness Function Definition
    • 3. Experimental Results and Illustrative Examples
    • 3.1. Experiment 1
    • 3.2. Experiment 2
    • 4. Conclusion
    • Acknowledgements
    • References
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