•Journal•ISSN: 1598-2645
The International Journal of Fuzzy Logic and Intelligent Systems
Korean Institute of Intelligent Systems
About: The International Journal of Fuzzy Logic and Intelligent Systems is an academic journal published by Korean Institute of Intelligent Systems. The journal publishes majorly in the area(s): Fuzzy logic & Fuzzy number. It has an ISSN identifier of 1598-2645. It is also open access. Over the lifetime, 924 publications have been published receiving 4420 citations. The journal is also known as: International journal of fuzzy logic and intelligent systems.
Papers published on a yearly basis
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TL;DR: This work proposes a method to classify leaves using the CNN model, which is often used when applying deep learning to image processing, and is itself a self-learning technique used on large amounts of data.
Abstract: There are hundreds of kinds of trees in the natural ecosystem, and it can be very difficult to distinguish between them. Botanists and those who study plants however, are able to identify the type of tree at a glance by using the characteristics of the leaf. Machine learning is used to automatically classify leaf types. Studied extensively in 2012, this is a rapidly growing field based on deep learning. Deep learning is itself a self-learning technique used on large amounts of data, and recent developments in hardware and big data have made this technique more practical. We propose a method to classify leaves using the CNN model, which is often used when applying deep learning to image processing.
116 citations
TL;DR: An on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems using a nonlinear state-space model of the plant and a particle filtering algorithm to estimate the probability density function of the state in real-time.
Abstract: This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant (with unknown time-varying parameters) and a particle filtering (PF) algorithm to estimate the probability density function (pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life (RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.
83 citations
61 citations
54 citations
TL;DR: Fuzzy technique for order preference by similarity to ideal solution is developed for intuitionistic hesitant fuzzy set to solve multi-criteria decision making problem in group decision environment.
Abstract: Dealing with uncertainty is always a challenging problem. Intuitionistic fuzzy sets was presented to manage situations in which experts have some membership and non-membership value to assess an alternative. Hesitant fuzzy sets was used to handle such situations in which experts hesitate between several possible membership values to assess an alternative. In this paper, the concept of intuitionistic hesitant fuzzy set is introduced to provide computational basis to manage the situations in which experts assess an alternative in possible membership values and non-membership values. Distance measure is defined between any two intuitionistic hesitant fuzzy elements. Fuzzy technique for order preference by similarity to ideal solution is developed for intuitionistic hesitant fuzzy set to solve multi-criteria decision making problem in group decision environment. An example is given to illustrate this technique.
52 citations