On approximation of lattice-valued functions using lattice integral transforms

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Viec Bui Quoc , Michal Holčapek
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引用次数: 0

Abstract

This paper examines the approximation capabilities of lattice integral transforms and their compositions in reconstructing lattice-valued functions. By introducing an integral kernel Q on the function domain, we define the concept of a Q-inverse integral kernel, which generalizes the traditional inverse kernel defined as a transposed integral kernel. Leveraging these Q-inverses, we establish upper and lower bounds for a transformed version of the original function induced by the integral kernel Q. The quality of approximation is analyzed using a lattice-based modulus of continuity, specifically designed for functions valued in complete residuated lattices. Additionally, under specific conditions, we demonstrate that the approximation quality for extensional functions with respect to the kernel Q can be estimated through the integral of the square of Q, and in certain cases, these extensional functions can be perfectly reconstructed. The theoretical findings, illustrated through examples, provide a strong foundation for further theoretical advancement and practical applications.
用格积分变换逼近格值函数
本文研究了格积分变换及其组合在重构格值函数中的逼近能力。通过在函数域上引入积分核Q,定义了Q逆积分核的概念,将传统的逆核定义为转置积分核进行了推广。利用这些q逆,我们建立了由积分核q引起的原始函数的变换版本的上界和下界。近似的质量是使用基于格的连续性模来分析的,这是专门为在完全剩馀格中取值的函数设计的。此外,在特定条件下,我们证明了外延函数对核Q的近似质量可以通过Q的平方积分来估计,并且在某些情况下,这些外延函数可以被完美地重构。通过实例说明了这些理论发现,为进一步的理论发展和实际应用提供了坚实的基础。
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
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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