• Title/Summary/Keyword: fuzzy cellular neural networks

Search Result 5, Processing Time 0.019 seconds

GLOBAL EXPONENTIAL STABILITY OF BAM FUZZY CELLULAR NEURAL NETWORKS WITH DISTRIBUTED DELAYS AND IMPULSES

  • Li, Kelin;Zhang, Liping
    • Journal of applied mathematics & informatics
    • /
    • v.29 no.1_2
    • /
    • pp.211-225
    • /
    • 2011
  • In this paper, a class of bi-directional associative memory (BAM) fuzzy cellular neural networks with distributed delays and impulses is formulated and investigated. By employing an integro-differential inequality with impulsive initial conditions and the topological degree theory, some sufficient conditions ensuring the existence and global exponential stability of equilibrium point for impulsive BAM fuzzy cellular neural networks with distributed delays are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on the delay kernel functions and system parameters. It is believed that these results are significant and useful for the design and applications of BAM fuzzy cellular neural networks. An example is given to show the effectiveness of the results obtained here.

The Proposal of the Robust Fuzzy Wavelet Morphology Neural Networks Algorithm for Edge of Color Image (컬러 영상 에지에 강건한 퍼지 웨이브렛 형태학 신경망 알고리즘 제안)

  • Byun, Oh-Sung
    • Journal of the Korea Society of Computer and Information
    • /
    • v.12 no.2 s.46
    • /
    • pp.53-62
    • /
    • 2007
  • In this paper, it can propose that Fuzzy Wavelet Morphology Neural Networks for the edge detection algorithm with being robustly a unclear boundary parts by brightness difference and being less sensitivity on direction to be detected the edges of images. This is applying the Fuzzy Wavelet Morphology Operator which can be simple the image robustly without the loss of data to DTCNN Structure for improving defect which carrys out a lot of operation complexly. Also, this color image can segment Y image with YCbCr space color model which has a lossless feature information of edge boundary sides effectively. This paper can offer the simulation of color images of 50ea for the performance verification of the proposal algorithm.

  • PDF

Fuzzy ART Neural Network-based Approach to Recycling Cell Formation of Disposal Products (Fuzzy ART 신경망 기반 폐제품의 리싸이클링 셀 형성)

  • 서광규
    • Journal of the Korea Safety Management & Science
    • /
    • v.6 no.2
    • /
    • pp.187-197
    • /
    • 2004
  • The recycling cell formation problem means that disposal products are classified into recycling product families using group technology in their end-of-life phase. Disposal products have the uncertainties of product condition usage influences. Recycling cells are formed considering design, process and usage attributes. In this paper, a new approach for the design of cellular recycling system is proposed, which deals with the recycling cell formation and assignment of identical products concurrently. Fuzzy ART neural networks are applied to describe the condition of disposal product with the membership functions and to make recycling cell formation. The approach leads to cluster materials, components, and subassemblies for reuse or recycling and can evaluate the value at each cell of disposal products. Disposal refrigerators are shown as an example.

A STUDY ON DTCNN APPLYING FUZZY MORPHOLOGY OPERATORS (퍼지 형태학 연산자를 적용한 DTCNN 연구)

  • 변오성;문성룡
    • Proceedings of the IEEK Conference
    • /
    • 2000.11c
    • /
    • pp.13-16
    • /
    • 2000
  • This paper is to compare DTCNN(Discrete-time Cellular Neural Networks) applying the fuzzy morphology operators with the conventional FCNN(Fuzzy CNN) using the general morphology operators. These methods are to the image filtering, and are compared as MSE. Also the main goal of this paper is to compare the fuzzy morphology operators with the general morphology operators through image input. In a result of computer simulation, we could know that the error of DTCNN applying the fuzzy morphology operators is less about 6.1809 than FCNN using the general morphology operators in the image included 10% noise, also the error of the former is less about 5.5922 than the latter in the image included 20% noise. And the image of DTCNN applying the fuzzy morphology operators is superior to FCNN using the general morphology operators.

  • PDF

Machine Cell Formation using A Classification Neural Network

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.4 no.1
    • /
    • pp.84-89
    • /
    • 2004
  • The machine cell formation problem is the problem to group machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts, and inter-cell part movements in cellular manufacturing systems and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net which is a kind of neural network model. To show the applicability of the proposed method, it presents some experiment results and compares the method with other cell formation methods. From the experiments, we observed that the proposed method could produce good cells for the machine cell formation problem.