• DocumentCode
    816413
  • Title

    FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC

  • Author

    Sim, J. ; Tung, W.L. ; Chai Quek

  • Author_Institution
    Sch. of Electr. Eng., Nanyang Technol. Univ.
  • Volume
    17
  • Issue
    6
  • fYear
    2006
  • Firstpage
    1394
  • Lastpage
    1410
  • Abstract
    The cerebellum is a brain region important for a number of motor and cognitive functions. It is able to generate error correction signals to drive learning and for the acquisition of memory skills. The cerebellar model articulation controller (CMAC) is a neural network inspired by the neurophysiologic theory of the cerebellum and is recognized for its localized generalization and rapid algorithmic computation capabilities. The main deficiencies in the basic CMAC structure are: 1) it is difficult to interpret the internal operations of the CMAC network and 2) the resolution (quantization) problem arising from the partitioning of the input training space. These limitations lead to the synthesis of a fuzzy quantization technique and the mapping of a fuzzy inference scheme onto the CMAC structure. The discrete incremental clustering (DIC) technique is employed to alleviate the quantization problem in the CMAC structure, resulting in the fuzzy CMAC (FCMAC) network. The Yager inference scheme (Yager), which possesses firm fuzzy logic foundation and maps closely to the logical implication operations in the classical (binary) logic framework, is subsequently mapped onto the FCMAC structure. This results in a novel fuzzy neural architecture known as the fuzzy cerebellar model articulation controller-Yager (FCMAC-Yager) system. The proposed FCMAC-Yager network exhibits learning and memory capabilities of the cerebellum through the CMAC structure while emulating the human way of reasoning through the Yager. The new FCMAC-Yager network employs a two-phase training algorithm consisting of structural learning based on the DIC technique and parameter learning using hebbian learning (associative long-term potentiation). The proposed FCMAC-Yager architecture is evaluated using an extensive suite of real-life applications such as highway traffic-trend modeling and prediction and performing as an early warning system for bank failure classification and medical diagnosis of breast can- er. The experimental results are encouraging
  • Keywords
    Hebbian learning; cerebellar model arithmetic computers; fuzzy logic; fuzzy neural nets; fuzzy reasoning; FCMAC-Yager; Hebbian learning; Yager-inference-scheme; bank failure classification; breast cancer; cerebellar model articulation controller; discrete incremental clustering; fuzzy inference; fuzzy logic; fuzzy quantization technique; highway traffic-trend modeling; localized generalization; medical diagnosis; neurophysiologic theory; warning system; Brain modeling; Computer networks; Error correction; Fuzzy control; Fuzzy logic; Fuzzy systems; Network synthesis; Partitioning algorithms; Quantization; Signal generators; Bank failure prediction; FCMAC; Yager inference scheme (Yager); breast cancer diagnosis; localized learning; traffic modeling and prediction; Algorithms; Biomimetics; Cerebellum; Fuzzy Logic; Humans; Information Storage and Retrieval; Learning; Models, Neurological; Nerve Net; Neural Networks (Computer); Pattern Recognition, Automated;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2006.880362
  • Filename
    4012043