Use of a Modified Threshold Function in Fuzzy Cognitive Maps for Improved Failure Mode Identification

IF 2.6 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Processing Letters Pub Date : 2024-05-09 DOI:10.1007/s11063-024-11623-y

Manu Augustine, Om Prakash Yadav, Ashish Nayyar, Dheeraj Joshi

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Abstract

Fuzzy cognitive maps (FCMs) provide a rapid and efficient approach for system modeling and simulation. The literature demonstrates numerous successful applications of FCMs in identifying failure modes. The standard process of failure mode identification using FCMs involves monitoring crucial concept/node values for excesses. Threshold functions are used to limit the value of nodes within a pre-specified range, which is usually [0, 1] or [-1, + 1]. However, traditional FCMs using the tanh threshold function possess two crucial drawbacks for this particular.Purpose(i) a tendency to reduce the values of state vector components, and (ii) the potential inability to reach a limit state with clearly identifiable failure states. The reason for this is the inherent mathematical nature of the tanh function in being asymptotic to the horizontal line demarcating the edge of the specified range. To overcome these limitations, this paper introduces a novel modified tanh threshold function that effectively addresses both issues.

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在模糊认知图中使用修正阈值函数改进故障模式识别

模糊认知图（FCM）为系统建模和仿真提供了一种快速高效的方法。文献显示，模糊认知图在故障模式识别方面的成功应用不胜枚举。使用 FCM 进行故障模式识别的标准流程包括监测关键概念/节点值是否超标。阈值函数用于将节点值限制在预先指定的范围内，该范围通常为[0, 1]或[-1, + 1]。然而，使用 tanh 阈值函数的传统 FCM 对这一特定目的而言有两个关键缺点：(i) 容易降低状态向量分量的值，(ii) 可能无法达到具有清晰可辨故障状态的极限状态。究其原因，是 tanh 函数的固有数学性质，即它与划定指定范围边缘的水平线近似。为了克服这些局限性，本文引入了一种新的修正 tanh 阈值函数，以有效解决这两个问题。

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

Neural Processing Letters 工程技术-计算机：人工智能

CiteScore

4.90

自引率

12.90%

发文量

392

审稿时长

2.8 months

期刊介绍： Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters