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Computational Intelligence Magazine, IEEE

Issue 3 • Date Aug. 2012

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Displaying Results 1 - 19 of 19
  • [Front cover]

    Publication Year: 2012 , Page(s): C1
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  • 2013 IEEE International Conference on Fuzzy Systems

    Publication Year: 2012 , Page(s): C2
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  • [Table of Contents]

    Publication Year: 2012 , Page(s): 1
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  • CIM Editorial Board

    Publication Year: 2012 , Page(s): 2
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  • Type-2 Fuzzy Logic - Plodding on Steadily and Staying Relevant [Editor's Remarks]

    Publication Year: 2012 , Page(s): 2 - 8
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  • Publish or Perish, but Where? [President's Message]

    Publication Year: 2012 , Page(s): 3
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  • CIS Society Officers

    Publication Year: 2012 , Page(s): 3
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  • Student Member Activities: Learning, Networking, Volunteering [Society Briefs]

    Publication Year: 2012 , Page(s): 4 - 6
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  • A New Web Home for the IEEE CIS [Society Briefs]

    Publication Year: 2012 , Page(s): 6 - 8
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  • IEEE CIS Philippines Chapter Report [Family Corner]

    Publication Year: 2012 , Page(s): 9 - 10
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  • CIS Publication Spotlight [Publication Spotlight]

    Publication Year: 2012 , Page(s): 11 - 12
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  • IEEE Congress on Evolutionary Computation

    Publication Year: 2012 , Page(s): 13
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  • Towards the Wide Spread Use of Type-2 Fuzzy Logic Systems in Real World Applications

    Publication Year: 2012 , Page(s): 14 - 24
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6897 KB) |  | HTML iconHTML  

    Real world applications are characterized by high levels of linguistic and numerical uncertainties. Since the inception of Fuzzy Logic Systems (FLSs), they have been applied with great success to numerous real world applications. The vast majority of FLSs so far have been traditional type-1 FLSs. However, type-1 FLSs cannot fully handle the high levels of uncertainties available in the vast majority of real world applications. This is because type-1 FLSs employ crisp and precise type-1 fuzzy sets. A type-2 FLS can handle higher uncertainty levels to produce improved performance. This paper follows on [1] to show how type-2 FLSs are starting to find their way into a variety of real world applications, promising a continuous growth both in number and variety of type-2 FLS applications in the next decade. View full abstract»

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  • Application of Type-2 Fuzzy Logic Filtering to Reduce Noise in Color Images

    Publication Year: 2012 , Page(s): 25 - 35
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5177 KB) |  | HTML iconHTML  

    In this paper, we present a novel application of type-2 fuzzy logic to the design of an image processing operator called an impulse detector. The type-2 fuzzy logic based impulse detector can be used to guide impulse noise removal filters to significantly improve their filtering performance and enhance their output images. The design of the proposed impulse detector is based on two 3-input 1-output first order Sugeno type interval type-2 fuzzy inference systems. The internal parameters of the type-2 fuzzy membership functions of the systems are determined by training. The performance of the impulse detector is evaluated by using it in combination with four popular impulse noise filters from the literature on four different popular test images under three different noise conditions. The results demonstrate that the type-2 fuzzy logic based impulse detector can be used as an efficient tool to effectively improve the performances of impulse noise filters and reduce the impulse noise undesirable distortion effects. View full abstract»

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  • Challenges for Perceptual Computer Applications and How They Were Overcome

    Publication Year: 2012 , Page(s): 36 - 47
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2217 KB) |  | HTML iconHTML  

    Perceptual Computing is a methodology of Computing with Words (CWW) to assist humans in making subjective judgments. This article introduces the Perceptual Computer (Per-C), our instantiation of Perceptual Computing. Per-C consists of three components: encoder, CWW engine and decoder. Perceptionswordsactivate the Per-C and are the Per-C output (along with data); so, it is possible for a human to interact with the Per-C using just a vocabulary. The encoder transforms words into fuzzy sets and leads to a codebookwords with their associated fuzzy set models. The outputs of the encoder activate a CWW engine whose output is one or more other fuzzy sets, which are then mapped by the decoder into a recommendation (subjective judgment) with supporting data. The recommendation may be in the form of a word, group of similar words, rank or class. When the Per-C was applied to actual applications, challenges occurred that had to be overcome. In this article we describe three applications (investment decision making, social judgment making, and distributed decision making), the challenges encountered and how they were overcome. View full abstract»

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  • Type-2 Fuzzy Logic: Challenges and Misconceptions [Discussion Forum]

    Publication Year: 2012 , Page(s): 48 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (738 KB) |  | HTML iconHTML  

    This material is based on contributions by a number of people including Janet Aisbett, Humberto Bustince, Oscar Castillo, Jonathan M. Garibaldi, Hani Hagras, Vladik Kreinovich, Jerry M. Mendel, Frank Rhee, Terry Rickard, Woei Wan Tan and Christian Wagner including notably discussions at a Panel Session at the 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems organized by Garibaldi, Hagras and Rhee. This paper focuses on the challenges of theoretical underpinning including notation, learning type-2 sets and systems and broadening the areas where type-2 system are applied. The misconceptions we discuss are around computational performance of type-2 systems and notion that type-2 systems are intended to replace type-1 fuzzy systems. View full abstract»

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  • Military Fleet Mix Computation and Analysis [Application Notes]

    Publication Year: 2012 , Page(s): 53 - 61
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (980 KB) |  | HTML iconHTML  

    A major financial expense for any military is the acquisition, operation, and maintenance of vehicles such as ships [1] and aircraft [2]. For example, the U.S. Air Force estimates that the acquisition of the F-35 fighter aircraft will cost $156 million each [3], hence even slight improvements in fleet efficiency and/or effectiveness can save governments large amounts of money or, using the same budget, can buy better equipment. Such high costs have driven the development and application of optimization and simulation methodologies to problems of military fleet mix computation and analysis. The complexity of military fleet mix problems, due in large part to the uncertainty, multi-objectivity, and temporal criticality of military missions, has resulted in the increased use of computational intelligence (CI) methods for solving them. View full abstract»

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  • Evolved to Win (Sipper, M.; 2011) [Book Review]

    Publication Year: 2012 , Page(s): 62 - 64
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  • [Conference Calendar]

    Publication Year: 2012 , Page(s): 63 - 64
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope