Turbo码概率-代数联合解码算法

Turbo码采用修正的BAHL et al.算法实现解码.这是一种基于软值的概率迭代解码算法.本文在保持Turbo码迭代软解码算法优点的基础上,充分利用Turbo码编码器结构这一确知条件,结合代数解码原理,提出了一种Turbo码概率-代数联合解码算法.该算法结合了概率解码和代数解码的优点,又有效避免了误差传播的发生,使Turbo码的纠错性能在原经典算法的基础上得到进一步的提高.该算法不仅为降低Turbo码的比特误码率和误差地板值提供了一种新的研究途径,而且因其更好的纠错性能而具有十分明显的实用价值.仿真实验结果显示,在比特误码率(BER)为10^-3~10^-4时,与经典Turbo码解码算法相比,采用该算法能获得0.1dB左右的编码增益.     

PERFORMANCE OF THREE STAGE TURBO－EQUALIZATION－DECODING

An increasing demand for high data rate transmission and protection over bandlimited channels with severe inter-symbol interference has resulted in a flurry of activity to improve channel equalization. In conjunction with equalization, channel coding-decoding can be employed to improve system performance. In this letter, the performance of the three stageturbo-equalization-decoding employing log maximum a posteriori probability is experimentally evaluated by a fading simulator. The BER is evaluated using various information sequence and interleaver sizes taking into account that the communication medium is a noisy inter symbol interference channel.     

线性分组码的神经网络译码

介绍了神经网络与线性分组码之间的关系，并在文献[1]的基础上证明了软判决译码与求解能量函数最大值之间的等价性，然后以(7，4，3)汉明码为例介绍了神经网络在循环码硬判决、软判决译码中的应用。     

A Bluetooth Routing Protocol Using Evolving Fuzzy Neural Networks

In this paper, a routing protocol which utilizes the characteristics of Bluetooth technology is proposed for Bluetooth-based mobile ad hoc networks. The routing tables are maintained in the master devices and the routing zone radius for each table is adjusted dynamically by using evolving fuzzy neural networks. Observing there exists some useless routing packets which are helpless to build the routing path and increase the network loads in the existing ad hoc routing protocols, we selectively use multiple unicasts or one broadcast when the destination device is out of the routing zone radius coverage of the routing table. The simulation results show that the dynamic adjustment of the routing table size in each master device results in much less reply time of routing request, fewer request packets and useless packets compared with two representative protocols, Zone Routing Protocol and Dynamic Source Routing.     

自适应RBF神经网络在CDMA移动通信上行链路功率控制中的应用研究

提出了一种自适应RBF神经网络功率控制方案。详细研究了该网络在DS-CDMA通信中，进行上行链路闭环功率控制(基于信扰比(SIR))的应用理论，给出了该网络参数的计算方法。最后用计算机仿真法模拟出该控制器的运行性能。结果表明基于SIR的自适应RBF神经网络功率控制器能自适应地调整移动台的发射功率，使基站接收信号的信扰比始终非常接近于一个常数，且有比定步长功率控制更小的SIR跟踪误差，从而可以降低接收信号的中断概率、提高信道容量。     

用基函数神经网络实现多阈值图象分割

本文介绍了一种用基函数神经网络实现多阈值图象分割的新方法。它从函数逼近的角度研究基于灰度直方图的多阈值分割问题，提出了一种模糊反向传播学习算法，采用该算法的高斯基函数网络能够准确检测直方图中包含的子区域和它们的分布函数，而且速度很快。实验表明本文的方法在实际图象分割中是有效的。     

人工神经元网络测井解释软件系统

王大力，何清源．人工神经元网络测井解释软件系统．测井技术，１９９４，１８（４）：２５７～２６４在复杂岩性地层的测井解释中，常规的方法常把岩石的矿物成分设想得比较简单，评价地层岩性时需要大量的人工分析。本文提出运用人工神经元网络理论的测井解释软件系统。系统用自然伽玛能谱测井，结合实验室资料可准确确定粘土类型和求出粘土含量。选用了一种稳定性好、快速的优化算法，使用孔隙度测井资料求出孔隙度值，整个系统算法上力求快准、解释上力求尽可能减少人工干预，该软件系统经试用效果较好。     

多变量系统故障诊断和容错控制新方法及其在精馏过程中的应用

本文提出一种多变量系统的故障诊断和容错控制新方法，整个系统由四个神经元组成。将本文方法用于二元精馏塔顶和塔底组分的容错控制，仿真结果证实了本文方法的有效性。该方法可望在难以建模的复杂化工过程中得到应用。     

神经网络离散变结构控制的算法及其应用

本文首次将自适应线性神经元（Ａｄａｌｉｎｅ）用于离散变结构控制。以滑模面Ｓ（ｋ）＝０作为Ａｄａｌｉｎｅ元的希望输出，作为Ａｄａｌｉｎｅ元的实际输出，通过ＬＭＳ算法在线调整权值可保证滑态可达条件的满足。应用所提出的控制策略对泵控马达速度伺服系统进行控制，仿真表明控制效果是令人满意的。     

Hierarchical intelligent control with flexible AC transmission systems application

A novel hierarchical intelligent controller configuration is proposed using an artificial neural network as a control-mode classifier in the supervisory level and a set of pre-designed controllers in the lower level. Controller outputs are modified nonlinearly by the classifying signals in a structure resembling one artificial neuron with adaptively changed weights. The lower-level local controllers are implemented using neural networks. An illustrative example of this approach is based on the transient stabilization of a single-machine infinite-bus system studied in Flexible AC Transmission Systems (FACTS) research.