变尺度因子暂态混沌神经网络多用户检测器

提出一种变尺度因子暂态混沌神经网络,具有较好的逃逸局部最优点的能力,并将其用于实现DS/CD-MA通信系统中的最佳多用户检测器.实验结果表明这种基于变尺度因子混沌神经网络的多用户检测器,其误码率性能优于已有的神经网络多用户检测器,能较好地逼近最佳多用户检测器的性能。

Multi-user Detector Using Timevarying Scaling-parameter Transiently Chaotic Neural Network

The existing neural network multi-user detectors are often trapped in the local minima, resulting in the performance degradation. In this paper, a timevarying scaling-parameter transiently chaotic neural network (TSTCNN) is proposed. The TSTCNN network has powerful capability to escape from getting into the local minima. The TSTCNN network is applied to the optimum detection problem in DS/CDMA systems. Numerical results show that the TSTCNN-based detector can perform better than the existing neural network detectors. The proposed detector can approximate to the optimum detector closely.