Kolmogorov–Arnold neural network for identification of functional groups from FTIR spectra

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-05-17 DOI:10.1016/j.chemolab.2025.105421

Tomasz Urbańczyk , Jakub Bożek , Szymon Mirczak , Jarosław Koperski , Marek Krośnicki

引用次数: 0

Abstract

New architecture of a deep neural network for identification of functional groups of molecules based on FTIR spectra is presented. The architecture employs the innovative Kolmogorov–Arnold layers. Instead of a single weight, each input in neurons belonging to these layers, possesses an independent learnable activation function. The article analyzes the quality of the neural network prediction for convolutional network containing Kolmogorov–Arnold layers in comparison with a classic convolutional neural network for 22 functional groups. The obtained results are compared with the results available from other studies.

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从FTIR光谱中识别官能团的Kolmogorov-Arnold神经网络

提出了一种基于红外光谱识别分子官能团的深度神经网络的新结构。建筑采用创新的Kolmogorov-Arnold层。而不是一个单一的权重，每个输入神经元属于这些层，拥有一个独立的可学习的激活函数。本文分析了包含Kolmogorov-Arnold层的卷积网络的神经网络预测质量，并与经典的卷积神经网络对22个功能群的预测进行了比较。所得结果与其他研究结果进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.