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An adaptive evolutionary modular neural network with intermodule connections

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Abstract

To approach the brain-like neural network and further improve the performance of the modular neural network (MNN), an adaptive evolutionary modular neural network with intermodule connections (EA-ICMNN) is proposed in this study. The EA-ICMNN is composed of a group of multilayer neural networks. Unlike traditional MNNs, in addition to the intramodule connections of subnetworks, intermodule connections are built for EA-ICMNN. All the parameters of the EA-ICMNN are learned by the improved Levenberg–Marquardt algorithm, and the optimal structure is adaptively determined by the improved mutation operator in the multiobjective optimization algorithm NSGAII. To verify the effectiveness of the proposed model, the EA-ICMNN is tested on several benchmark datasets and a practical prediction problem for biochemical oxygen demand in wastewater treatment process. The experimental results show that the proposed model has better generalization ability than other MNNs and that its structure is simplified by its sparse intermodule connections.

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Data availability

1. The datasets generated during and/or analyzed during the current study are available in the UCI machine learning repository(http://archive.ics.uci.edu/) and the Kaggle datasets repository(https://www.kaggle.com/datasets).

2. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Key Research and Development Program of China [No. 2021ZD0112301]; National Natural Science Foundation of China [Nos. 62021003, 61890930–5, and 62173008].

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China

    Meng Li, Wenjing Li, Zhiqian Chen & Junfei Qiao

  2. CRSC Urban Rail Transit Technology Co., Ltd., Beijing, 100070, China

    Meng Li

  3. Beijing Laboratory of Smart Environmental Protection, Beijing, 100124, China

    Meng Li, Wenjing Li, Zhiqian Chen & Junfei Qiao

  4. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, 100124, China

    Meng Li, Wenjing Li, Zhiqian Chen & Junfei Qiao

  5. Beijing Institute of Artificial Intelligence, Beijing, 100124, China

    Meng Li, Wenjing Li, Zhiqian Chen & Junfei Qiao

  6. Beijing Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Meng Li, Wenjing Li, Zhiqian Chen & Junfei Qiao

Authors
  1. Meng Li
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  2. Wenjing Li
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Contributions

Meng Li: Conceptualization, Methodology, Writing-original draft, Software, Investigation. Wenjing Li: Validation, Writing—review & editing, Funding acquisition. Zhiqian Chen: Data curation, Software. Junfei Qiao: Writing—review & editing, Supervision, Funding acquisition.

Corresponding author

Correspondence to Junfei Qiao.

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Li, M., Li, W., Chen, Z. et al. An adaptive evolutionary modular neural network with intermodule connections. Appl Intell 54, 4121–4139 (2024). https://doi.org/10.1007/s10489-024-05308-1

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