# A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Article · Yiming Jiang, Chenguang Yang, Jing Na, Guang Li, Yanan Li, Junpei Zhong

- Archive id: doi:10.1155/2017/1895897
- Published: 2017-01-01
- Venue: Complexity
- DOI: https://doi.org/10.1155/2017/1895897
- Citations: 74
- Source URL: https://doi.org/10.1155/2017/1895897
- PDF: http://downloads.hindawi.com/journals/complexity/2017/1895897.pdf
- Provenance: openalex

## Abstract

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [Robot Learning & Embodied AI](https://space-kitty.com/topics/robotics-embodied)

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