Yazar "Liu, Fang" seçeneğine göre listele

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    Coding Method Based on Fuzzy C-Means Clustering for Spiking Neural Network With Triangular Spike Response Function
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Liu, Fang; Pedrycz, Witold; Zhang, Chao; Yang, Jie; Wu, Wei
    Although spiking neural network (SNN) has the advantages of strong brain-likeness and low energy consumption due to the use of discrete spikes for information representation and transmission, its performance still needs to be improved. This article improves SNN in terms of the coding process and the spike response function by invoking fuzzy sets. In terms of coding, a new fuzzy C-means coding (FCMC) method is proposed, which breaks the limitation of uniformly distributed receptive fields of existing coding methods and automatically determines suitable receptive fields that reflect the density distribution of the input data for encoding through the fuzzy C-means clustering. In terms of spike response function, triangular fuzzy numbers instead of the commonly used alpha-type function are used as the spike response function. Different from other functions of fixed shape, width parameters of the proposed function are learnt in the iterative way like weights of synapses do. Experimental results obtained on seven benchmark datasets and two real-world datasets with eleven approaches demonstrate that SNN with triangular spike response functions (abbreviated as T-SNN) combining FCMC can achieve improved performance in terms of accuracy, F-measure, AUC, required epochs, running time, and stability.
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    A two-objective-optimization-driven group decision making model under the bipolarity of decision information
    (Elsevier, 2024) Luo, Ziqian; Liu, Fang; You, Qirui; Pedrycz, Witold
    When building consensus in group decision making (GDM) under uncertainty, an important yet rarely studied issue is to find the Pareto solutions of multi -objective optimization model. This paper reports a two -objective (2Ob) optimization driven consensus model in GDM by describing the bipolarity of judgements through intuitionistic multiplicative preference relations (IMPRs). First, it is realized that the inherent property of IMPRs is the hesitancy degree. A novel inconsistency index of IMPRs is proposed by combining the effects of hesitancy degree and inconsistency of boundary matrices. Second, the compatibility measure between two IMPRs is utilized to quantify the consensus level (CL) of decision makers. The threshold of acceptable group CL is found to decrease with the order of IMPRs for the first time. A 2Ob optimization model is constructed by minimizing group inconsistency degree and group CL, respectively. Third, the method of equipping two flexibility degrees to each expert is proposed for optimizing individual IMPRs. It is interesting to find that the constructed granularity matrix is different from interval -valued IMPRs. A multi -objective particle swarm optimization algorithm is adopted to obtain a set of Pareto solutions to GDM problems. Case studies are carried out to illustrate the proposed consensus reaching model. The results help to identify how to provide flexible decisions in GDM under some complexity and uncertainty of a practical problem.