随机Petri网同步并发行为的定量分析方法

传统的随机Ｐｅｔｒｉ网定量分析都是针对顺序行为的，而并发描述却是Ｐｅｔｒｉ网引以为骄傲的内容，这就形成Ｐｅｔｒｉ擅长描述并发，却没有在并发活动下分析系统的手段。本文研究同步并发机制下随机Ｐｅｔｒｉ网定量分析法，旨在改进传统的分析方法，使Ｐｅｔｒｉ网真正成为并发系统建模与分析的有力工具。     

基于Petri网语言的系统设计与分析

本文从Ｐｅｔｒｉ网语言着手，研究系统设计方法，提出相应的理论，建立有关策略，形成一套从用户需求到原型Ｐｅｔｒｉ网模型以及对模型的性质分析和控制的方法。通过对系统的逻辑行为及性能指标的分析比较，从而优选出好的设计方案     

基于随机高级Petri网的ATM网络接纳控制过程模型

本文提出了一个ＡＴＭ网络的接纳控制过程模型，此模型是基于随机高级Ｐｅｔｒｉ网的层次模型方法。在复杂的ＡＴＭ网络的接纳控制过程模型中，使用了从顶向下的模型方法。在模型的性能分析中，则采用了从底向上的分解、压缩的方法。这种方法简化了复杂系统模型的状态空间爆炸问题。     

同步时序电路的增广Petri网分析

本文应用抑制弧的增广Ｐｅｔｒｉ网建立了基本门电路和常用触发器的Ｐｅｔｒｉ网模型；并运用该模型描述了同步时序电路；提出了增广Ｐｅｔｒｉ网的授权矩阵、状态转移方程和触发展次态与变迁授权条件的关系，在此基础上可对同步时序电路描述和分析，并用实例证明了该方法的有效性。     

Petri网在电信管理网可信性建模中的应用

介绍了ＴＭＮ（电信管理网）可信性建模的一种新的方法－Ｐｅｔｒｉ网。首先简要介绍了Ｐｅｔｒｉ网的基本概念和Ｐｅｔｒｉ网的特性分析，然后描述了用Ｐｅｔｒｉ网进行建模的一般方法并举例说明了它在ＴＭＮ可信性建模中的应用。最后还介绍了一种自动生成可信性Ｐｅｔｒｉ网模型的方法。     

Petri网在帧中继与X.25协议转换中的应用

提出了一种用Ｐｅｔｒｉ网研究两种协议转换的方法。在两个提供原语的基础上，利用Ｐｅｔｒｉ网丰富的描述特性建立转换模型，与用有限状态机建立转换模型相比，简化了过程。最后，用这种方法分析了Ｘ．２５与帧中继之间的协议转换，并建立转换模型。     

局域网服务器缓冲区容量的Petri网设计方法

本文在分析局域网服务器信息缓冲区、数据包接受和发送缓冲区的相互关系基础上提出一种新的局域网服务器缓冲区设计方法─—广义随机Ｐｅｔｒｉ网方法，并给出了局域网服务器信息缓冲区的广义随机Ｐｅｔｒｉ网模型。该设计方法理论完备、严谨实用，且不再局限于追求某一边界值（上限或下限），并且避免了排队理论处理多级服务的繁琐公式。最后本文用一例题说明了这种方法的应用。     

局域服务器缓冲区容量的Petri网设计方法

本文在分析局域服务器服务缓冲区、数据包接受和发送缓冲区的相互关系基础上提出一种新的局域网服务器缓冲区设计方法－广义随机Ｐｅｔｒｉ网方法，并给出了局域网服务器信息缓冲区的广义随机Ｐｅｔｒｉ网模型。该设计方法理论完备、严谨实用，且不再局限于追求某一边界值（上限或下限）并且避免了排队理论处理多级服务的繁琐公式。最后本文用一例题说明了这种方法的应用。     

局域网服务器缓冲器容量的Petri网设计法

本文在分析局域网服务器信息缓冲区、数据包接收和发送缓冲区的相互关系基础上提出了一种新的局域网服务器缓冲区设计方法－广义随机Ｐｅｔｒｉ网方法。并针对局域网服务器信息缓冲区给出了它的广义随机Ｐｅｔｒｉ网模型。该设计方法理论完备，严谨实用，且不再局限于追求某一边界值（上限或下限），并且避免了排队理论处理多级服务的繁琐公式。最后本文用一例题说明了这种方法的应用。     

集成电路功能测试的计算机辅助生成方法

介绍了一种ＶＬＳＩ功能测试生成的结构分析法。它采用Ｐｅｔｒｉ网作为测试序列的模型工具，通过简化Ｐｅｔｒｉ网选择不确定度最小的测试序列，以降低测试序列的复杂度，缩短计算时间。     

Parallel,self-organizing,hierarchical neural networks with forward-backward training

A forward-backward training algorithm for parallel, self-organizing hierarchical neural networks (PSHNNs) is described. Using linear algebra, it is shown that the forward-backward training of ann-stage PSHNN until convergence is equivalent to the pseudo-inverse solution for a single, total network designed in the least-squares sense with the total input vector consisting of the actual input vector and its additional nonlinear transformations. These results are also valid when a single long input vector is partitioned into smaller length vectors. A number of advantages achieved are: small modules for easy and fast learning, parallel implementation of small modules during testing, faster convergence rate, better numerical error-reduction, and suitability for learning input nonlinear transformations by other neural networks. The backpropagation (BP) algorithm is proposed for learning input nonlinearitics. Better performance in terms of deeper minimum of the error function and faster convergence rate is achieved when a single BP network is replaced by a PSHNN of equal complexity in which each stage is a BP network of smaller complexity than the single BP network.     

误差敏感竞争性学习算法

本文基于等误差准则提出了一种适用于矢量量化技术的新型码书设计算法。实验表明此算法优于现存算法。为解决初始码书赋值问题，本文提出了自生成自组织神经网络方法。实验表明此算法加速了算法的收敛速度，提高了算法的性能     

A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications

This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.     

Speech recognition algorithm based on neural network and hidden Markov model

This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model (HMM) and artificial neural network (ANN). First, the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second, with the probability score as input to the neural network, the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally, Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of neural network, the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover, the hybrid model enhances the individual flaws of the HMM and the neural network and greatly improves the speed and performance of speech recognition.     

Direction of arrival estimation based on phase differences usingneural fuzzy network

A new high-resolution direction of arrival (DOA) estimation technique using a neural fuzzy network based on phase difference (PD) is proposed. The conventional DOA estimation method such as MUSIC and MLE, are computationally intensive and difficult to implement in real time. To attack these problems, neural networks have become popular for DOA estimation. However, the normal neural networks such as the multilayer perceptron (MLP) and radial basis function network (RBFN) usually produce the extra problems of low convergence speed and/or large network size (i.e., the number of network parameters is large). Also, the may to decide the network structure is heuristic. To overcome these defects and take use of neural learning ability, a powerful self-constructing neural fuzzy inference network (SONFIN) is used to develop a new DOA estimation algorithm. By feeding the PDs of the received radar-array signals, the trained SONFIN can give high-resolution DOA estimation. The proposed scheme is thus called PD-SONFIN. This new algorithm avoids the need of empirically determining the network size and parameters in normal neural networks due to the powerful on-line structure and parameter learning ability of SONFIN. The PD-SONFIN can always find itself an economical network size in the fast learning process. Our simulation results show that the performance of the new algorithm is superior to the RBFN in terms of convergence accuracy, estimation accuracy, sensitivity to noise, and network size     

一种改进的Elman神经网络模型

本文首先详细地阐述了Elman神经网络的结构、原理和学习算法.为了进一步提高Elman神经网络的逼近能力和动态特性,我们提出了一种改进的Elman神经网络模型.这种新的Elman神经网络在关联节点与输出节点之间又增加了一组可调权值,利用误差回馈原理推导出了其相应的学习算法.仿真实验结果表明,改进的Elman神经网络比原来的网络具有更好的动态性能,对于贯序输入输出数据的逼近收敛速度更快.     

模糊对向传播神经网络的学习算法

模糊对向传播神经网络的学习算法由输入层至竞争层的连接权向量和竞争层到输出层的连接权向量两部分的学习组成,对于前者,分别选用聚类法和工下降法,本文研究了模糊对向传播神经网络的两种学习算法从理论上分析了这两种算法的性质,把算法应用于著名Ｍａｃｋｅｙ－Ｇｌａｓｓ混沌时间序列预测问题中,实验结果表明后一种算法的学习精度及泛化能力较前一种算法要好,但前者的学习速度要快。     

基于改进深度Q学习的网络选择算法

在引入休眠机制的超密集异构无线网络中,针对网络动态性增强,导致切换性能下降的问题,该文提出一种基于改进深度Q学习的网络选择算法.首先,根据网络的动态性分析,构建深度Q学习选网模型;其次,将深度Q学习选网模型中线下训练模块的训练样本与权值,通过迁移学习,将其迁移到线上决策模块中;最后,利用迁移的训练样本及权值加速训练神经...     

用HM框架下的神经网络分类器识别雷达目标

提出了一种HMM框架下的神经网络分类器,它既克服了普通神经网络不能有效地识别时变信号的缺点,又解决了HMM识别时变信号时不能突出不同信号的差异性问题。用网络权的遗传算法进化学习解决了Baum-Welch及BP网络学习中易陷入局部极小点的问题,还给出了用该网络成功识别实测雷达回波信号的实例。     

非线性对象神经网络建模的广义自组织学习

本文提出了非线性对象神经网络建模的广义自组织学习算法,该算法采用多个局部模型进行建模,扩展了Kohonen自组织学习算法中的局部模型划分机制,且多个局部模型的划分兼顾了输入样本的分布和模型匹配特性.仿真结果表明,广义自组织学习算法明显地提高了建模精度和收敛速度.