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Robot knowledge analysis based on cognitive computing and modular neural network feature combination

  • S.I. : Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT 2022)
  • Published:
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Abstract

With the ongoing integration of information technology and industrialization, strategic emerging industries are becoming an increasingly important force in guiding future economic and social development. As one of the strategic emerging industries' development priorities and a replacement for scarce labor resources, industrial robots will be widely used in labor-intensive industries. It is a contentious topic how to evaluate and improve robot knowledge education. There are modular features introduced in this paper, which builds a modular network for the evaluation of robot knowledge education quality and enhances the cognitive computing ability of artificial neural networks and their ability to process complex information. A modular neural network-based model of feature combination robot knowledge education quality evaluation is developed based on the three aspects of module division method, subnet structure selection, and feature combination output. The following are some of the paper's most important contributions: K-OD algorithm of density clustering optimized by K-means is proposed. Because of its high level of modularized partition simulating, this method has an excellent clustering effect, and it identifies the core points, boundary points, as well as outliers. Using K-OD algorithm, the calculation of density radius and threshold is optimized by using density clustering, which reduces the overall computational complexity. Find out how SOM neural networks learn from competition. SOM network's competition layer neuron weights are prone to falling into local optimal solutions, so an SASOM neural network with weight adjustment simulated by an annealing algorithm is proposed to address this issue. It is more accurate in terms of prediction and error, and it is better at identifying sample attribute features. This work builds a modular neural network for evaluating robot knowledge education quality using K-OD clustering algorithm and SASOM neural network, which introduces the simulated annealing mechanism.

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Data availability

The datasets used during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported by (1) Inner Mongolia Natural Science Foundation Project "Research on the Optimization of Rare Earth Industry Technology Innovation Ecosystem Based on System Dynamics in Inner Mongolia" (2020MS07004.3); (2) "System Dynamics Analysis of Rare Earth Industry Technology Innovation Ecosystem in Inner Mongolia" (2020NDB023.4)

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  1. Inner Mongolia University, Hohhot, 010020, Inner Mongolia, China

    Zhenliang Xu, Zhen Wang & Xi Chen

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  1. Zhenliang Xu
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  2. Zhen Wang
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Correspondence to Zhen Wang.

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Xu, Z., Wang, Z. & Chen, X. Robot knowledge analysis based on cognitive computing and modular neural network feature combination. Neural Comput & Applic 36, 2245–2260 (2024). https://doi.org/10.1007/s00521-023-08675-x

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