一、算法:问题与方法1.非标称情况下信息融合得益的描述(B.V. Dasarathy,美国)2.用于提高身份验证系统性能的多级数据融 合方法(P.S.Verlinde等,比利时)3.采用预期输出隶属关系函数的自适应数据 融合(J.Choi,美国)4.采用一种预期实用间隔决策规则的改进型 Dempster-Shafer方法(A.Cheaito等, 加拿大)5.虚拟联想网络:传感器融合的一种新的范例 I. Algorithms: Problems and Methods 1. Description of Benefits of Information Fusion in Non-Nominal Cases (BV Dasarathy, USA) 2. Multi-Level Data Fusion Methodologies for Improving the Performance of Authentication Systems (PS Verlinde et al., Belgium). Adaptive Data Fusion Using Expected Output Affinity Functions (J. Choi, U.S.A.) 4. Improved Dempster-Shafer Approach Using Expected Practical Interval Decision Rules (A. Cheaito et al., Canada) 5. Virtual Associative Networks: Sensors A new paradigm of convergence