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Target Identification Based on Neural Network and D-S Evidence Theory

机译:基于神经网络和D-S证据理论的目标识别

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This paper presents a method of multisensor data fusion based on neuron network and reasoning (Dempster-Shafer evidence reasoning).The method can use deal with the inaccuracy and fuzzy information by D-S Evidence. And also it can give a full play to self-study of neural net, self-adapting and fault tolerant ability. In this way it has doughty robustness to uncertain information and improves the system identification rate. Then the D-S evidence is used to fuse the results derived from the neural network at different time. The result of computer simulation shows the method is effective and correct.
机译:本文提出了一种基于神经元网络和推理(Dempster-Shafer证据推理)的多传感器数据融合方法。该方法可以通过D-S证据处理不准确和模糊信息。并且可以充分发挥神经网络的自学,自适应能力和容错能力。这样,它对不确定信息具有鲁棒性,并提高了系统识别率。然后将D-S证据用于融合在不同时间从神经网络得出的结果。计算机仿真结果表明,该方法是正确有效的。

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