液体神经网络:工作原理和应用领域

R. Shevtsov, V. Bredikhin, I. Khoroshylova
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摘要

文章论述了液体神经网络(LNN)的结构及其在现代技术中的潜力。由于算法和硬件的不断发展,神经网络变得越来越强大和高效,这为其应用带来了新的机遇。作者介绍了液体神经网络的工作原理,其中包括学习和推理过程,从而可以有效利用系统的自然动态来解决各种任务,包括分类、预测和控制。我们注意到,液态神经网络的概念是为了克服传统神经网络所面临的一些局限和问题而产生的。本研究探讨了 LNN 的基本概念和原理及其在机器人、医学和工业等各个领域的应用潜力。研究还确定了 LNN 与传统模型相比的主要优缺点。LNN 可用于处理大量数据流,如视频、音频或来自各类传感器的感官数据,使机器人能够接收有关其环境的信息,并根据这些数据做出决策。在医疗诊断和图像处理方面,液态神经网络可大大提高诊断程序的质量和效率。液态神经网络可以实现自动控制系统,监测和调节生产过程参数,或适应环境变化,优化参数,以达到最高生产率和产品质量。LNN 领域缺乏标准,仅限于使用性能指标。建立标准和客观指标将使研究人员和工程师能够了解和比较不同的 LNN 实施情况。虽然 LNN 在功耗方面相对高效,但其在硬件层面的实现可能需要新的技术和架构来优化性能。因此,本研究概述了这一技术的进一步发展前景。关键词:液态神经网络、人工智能、自适应控制、学习效率、应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LIQUID NEURAL NETWORKS: PRINCIPLE OF WORK AND AREAS OF APPLICATION
The article deals with the architecture of liquid neural networks (LNN) and their potential in modern technologies. Thanks to the constant development of algorithms and hardware, neural networks are becoming more and more powerful and efficient, which opens up new opportunities for their application. The authors describe the principle of operation of liquid neural networks, which includes the process of learning and inference, which allows effective use of the natural dynamics of the system to solve various tasks, including classification, prediction, and control. We note that the concept of LNNs arose as an attempt to overcome some of the limitations and problems faced by traditional neural networks. The study considers the basic concepts and principles of LNNs and their application potential in various fields, from robotics to medicine and industry. The study also determines the main advantages and disadvantages of LNNs compared to traditional models. It is possible to use them to process a large stream of data, such as video, audio, or sensory data from various sensor types, allowing robots to receive information about their environment and make decisions based on that data. In medical diagnostics and image processing, liquid neural networks can significantly contribute to the quality and efficiency of diagnostic procedures. LNNs can enable the implementation of automatic control systems that monitor and regulate parameters of production processes or adapt to changes in the environment and optimise parameters to achieve maximum productivity and product quality. The field of LNN lacks standards and is limited to using performance metrics. Establishing standards and objective metrics will allow researchers and engineers to understand and compare different LNN implementations. Although LNNs are relatively efficient in terms of power consumption, their implementation at the hardware level may require new technologies and architectures to optimise performance. As a result, the study outlines the prospects for the further development of this technology. Keywords: liquid neural networks, artificial intelligence, adaptive control, learning efficiency, application potential.
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