关系排序的一种硬件实现

本文提出了一种利用硬件实现关系排序的模型，该模型采用总线相联的细胞阵列结构形式。它适宜于用ＶＬＳＩ实现，关系元组沿阵列管道边传输边排序，传输与排序同时完成，元组的串入串出满足计算机顺序存取的要求，利用ｎ个细胞对ｎ个元组排序只需２ｎ步，输入和输出各ｎ步，时延为零，尤其重要的是，输出的序列就是排了序的元组本身。     

关于迷宫排序问题的研究

本文首先把迷宫排序问题推广为ｍ×ｎ迷宫（ｍ＞１，ｎ＞１）的排序问题，证明了ｍ×ｎ迷宫的任一初始状态能经过有限步移动转变成目标状态的充要条件，然后给出了一个ｍ×ｎ迷宫排序的算法，该算法的时间复杂度是Ｏ（ｍｎ（ｍ＋ｎ）），空间复杂度是Ｏ（ｍｎ）．最后还指出了它的时间复杂度的一个下界．这样，关于迷宫排序问题就基本上得到了圆满地解决．     

二次堆排序算法和提高排序效率的途径

本文讨论了一种堆排序的改进算法，该算法的平均时间复杂度达到ｎｌｏｇ２ｎ＋Ｏ（ｎ）。在此基础上，提出了二次堆排序的算法，使该排序过程中优化数据处理，排序速度提高１８０％。同时，本文给出了提高效率的措施、排序算法和实验结果。最后，给出了快速排序的优化数据处理的途径，从而较大地提高了排序效率。     

基于部分同步计算的神经网络排序模型分析

本文将神经网络用于求解排序问题，建立了一个基于部分同步计算的人工神经网络排序模型。网络的无教师学习过程就是排序过程。文中还给出了元索个数很多时的排序策略。     

一种基于离散马尔科夫模型的神经网络可靠性设计方法

本文给出了一和循环前馈神经网络来表示具有ｎ个状态的离莠马尔科夫模型,及其二次能量函数,并根据梯度下降规则,给出了相应的权值修改规则。利用该神经网络方法可以有效地对容错硬件进行可靠性设计。     

基于散列和归并技术的有效并行排序方法

本文提出一个在共享存储多处理机系统上实现的快速、有效的并行排序算法：将长度为ｎ的待排序数据划分成ｐ个长度为ｎ／ｐ的子序列，引入散列技术并行地对这ｐ个子序列的数据进行二次散列排序，这一阶段所需的平均时间为Ｏ（ｎ／ｐ）；最后并行地将ｐ个有序子序列归并成一个长度为ｎ的有序序列，归并阶段所需的时间为Ｏ（ｎ－ｎ／
／ｐ）。整个排序算法的并行执行代价为Ｏ（ｎｐ）。本排序方法可以拓以网络并行机群环境。     

融合多种神经网络与多特征的答案排序方法

针对答案排序问题,提出并构建融合多种神经网络与多特征的答案排序模型.将问题和候选答案的词向量送入使用Leaky Relu激活函数的卷积神经网络进行学习,得到的学习结果与词汇特征、主题特征等相互拼接,输入到双向门控循环单元,其输出结果经由多层感知器进行处理后,通过softmax分类器得出最终答案排序的结果.实验结果表明,该模型在WikiQACorpus数据集上取得了较好实验结果,准确率略高于已有基线模型,达到74.43％.     

基于数据分布特性的快速排序

文中提出了一种基于数据分析特性的快速排序算法，根据被排数据的分布行性，选择数据比较次数和数据移动次数较少的排序算法，当被排数据存在ｍ个有序序列时，其算法的时间复杂度为Ｏ（ｎｌｏｇ２ｍ）其中ｍ∈（１，ｃｆ√ｎ），ｃ为某一常数，其最佳性能为Ｏ（ｎ）。当ｍ≥ｃ（√ｎ）时，保持快速排序的最佳平均性能，使排序运行于较优状态下。     

快速分布式数据库数据分布模型求解算法

针对我们提出的以最小代价为目标的分布式数据库数据分布模型，进行适当的数据分组，并对部份组进行排序，使单目运算，双目运算的计算复杂度分别提高了Ｏ（ｎ）和Ｏ（ｎ＾２）。更新运算分解后计算复杂度提高Ｏ（ｎ）。     

关系排序器的设计

本文提出了一种用于关系查询功能磁盘系统中的关系排序器的设计。基于无比较方式设计的这种排序器采用了分组排序与归并排序相结合的方式实现，其关键部件－最大值标记逻辑由０（［√ｎ］×ｍ）个基本逻辑门构成（ｎ为待排元组个数，ｍ为元组关键字的二进位数），结构简单、规整，适宜于用ＶＬＳＩ实现。     

Time- and VLSI-optimal sorting on enhanced meshes

Sorting is a fundamental problem with applications in all areas of computer science and engineering. In this work, we address the problem of sorting on mesh connected computers enhanced by endowing each row and each column with its own dedicated high-speed bus. This architecture, commonly referred to as a mesh with multiple broadcasting, is commercially available and has been adopted by the DAP family of multiprocessors. Somewhat surprisingly, the problem of sorting m, (m⩽n), elements on a mesh with multiple broadcasting of size √n×√n has been studied, thus far, only in the sparse case, where m∈Θ(√n) and in the dense case, where m∈ΘO(√n). Yet, many applications require using an existing platform of size √n×√n for sorting m elements, with √n½+ϵ⩽m⩽n, stored in the leftmost [m/√n] columns of a mesh with multiple broadcasting of size √n×√n in Θ(m/√n) time     

Neural network modelling of continuous microbial cultivation accounting for the memory effects

Three approaches to the problem of Neural Network (NN) modelling of chemostat microbial culture accounting for the memory effects are considered and, based on the results they are compared. The first approach uses feedforward NNs with time delay feedback connections from and to the output neurons, for the entire process modelling. The second and third approach relay on Hybrid NN modelling. The second one applies feedforward NNs with time delayed inputs for the specific growth rate approximation within the framework of the classical unstructured model. In this case the specific consumption rate is assumed to be proportional to the specific growth rate. The yield factor is assumed to be constant or polynomial function of the substrate concentration. The third approach is also based on a classical unstructured model, but different feedforward NNs with delay elements for both specific growth rate and specific consumption rate approximation are adopted. On the example of the growth of a strain Saccharomyces cerevisiae on a glucose limited medium different NN topologies are studied and a suitable model is figured out.     

网络化神经网络的时滞依赖稳定性判据

本文研究了网络化神经网络的稳定性问题.首先,为了利用网络系统的采样特征,定义了一个新的Lyapunov泛函;通过分析网络诱导时延和执行周期之间的关系,采用一个迭代凸组合技术,得到了一个包含较少保守性的稳定性判据.然后,给出一个基于采样数据的神经网络稳定性判据,减少了计算复杂性.最后,通过一个数例,验证了本文方法的有效性和优越性.     

Genetic design of biologically inspired receptive fields for neural pattern recognition

This paper proposes a new method for the design, through simulated evolution, of biologically inspired receptive fields in feedforward neural networks (NNs). The method is intended to enhance pattern recognition performance by creating new neural architectures specifically tuned for a particular pattern recognition problem. It proposes a combined neural architecture composed of two networks in cascade: a feature extraction network (FEN) followed by a neural classifier. The FEN is composed of several layers with receptive fields constructed by additive superposition of excitatory and inhibitory fields. A genetic algorithm (GA) is used to select receptive field parameters to improve classification performance. The parameters are receptive field size, orientation, and bias as well as the number of different receptive fields in each layer. Based on a random initial population where each individual represents a different neural architecture, the GA creates new enhanced individuals. The method is applied to handwritten digit classification and face recognition. In both problems, results show strong dependency between NN classification performance and receptive field architecture. GA selected parameters of the receptive fields produced improvements in the classification performance on the test set up to 90.8% for the problem of handwritten digit classification and up to 84.2% for the face recognition problem. On the same test sets, results were compared advantageously to standard feedforward multilayer perceptron (MLP) NNs where receptive fields are not explicitly defined. The MLP reached a maximum classification performance of 84.9% and 77.5% in both problems, respectively.     

How to sort N items using a sorting network of fixed I/O size

Sorting networks of fixed I/O size p have been used, thus far, for sorting a set of p elements. Somewhat surprisingly, the important problem of using such a sorting network for sorting arbitrarily large datasets has not been addressed in the literature. Our main contribution is to propose a simple sorting architecture whose main feature is the pipelined use of a sorting network of fixed I/O size p to sort an arbitrarily large data set of N elements. A noteworthy feature of our design is that no extra data memory space is required, other than what is used for storing the input. As it turns out, our architecture is feasible for VLSI implementation and its time performance is virtually independent of the cost and depth of the underlying sorting network. Specifically, we show that by using our design N elements can be sorted in Θ(N/p log N/p) time without memory access conflicts. Finally, we show how to use an AT²-optimal sorting network of fixed I/O size p to construct a similar architecture that sorts N elements in Θ(N/p log N/p log p) time     

基因组Translocation排序问题的改进多项式算法

该文给出基因组Transhocation排序问题的一个改进多项式算法，原算法所有存储空间O（n)，时间复杂度为O(n^3)，文中改进算法仍采用O(n)存储空间，时间复杂度为O(n^2logn),具体地，将计算Translocation距离的时间复杂度由O(n^3)改进为O(n^2),将计算Translocation序列的时间复杂度由O(n^3)改进为O(n^2logn).     

基于优势形式背景的对象全序化方法研究

对象全序化是基于优势关系的信息系统的一个重要内容,形式概念分析提供了对象排序的一个自然平台,然而传统的形式概念分析不能用来处理具有优势关系的属性。在优势概念格的基础上,对对象全序化方法进行了研究。基于优势关系形式背景,定义了对象在属性集上的优势度,进一步给出了对象的综合优势度,并利用综合优势度对对象进行全序化,并通过实例对方法进行了说明。研究进一步丰富了优势信息系统的相关理论。     

Bidirectional Conditional Insertion Sort algorithm; An efficient progress on the classical insertion sort

In this paper, we proposed a new efficient sorting algorithm based on insertion sort concept. The proposed algorithm is called Bidirectional Conditional Insertion Sort (BCIS). It is in-place sorting algorithm and it has remarkably efficient average case time complexity when compared with classical insertion sort (IS). By comparing our new proposed algorithm with the Quicksort algorithm, BCIS indicated faster average case time for relatively small size arrays up to 1500 elements. Furthermore, BCIS was observed to be faster than Quicksort within high rate of duplicated elements even for large size array.     

Distributed event-triggered optimal bipartite consensus control for multiagent systems with input delay via reinforcement learning method

In this article, an optimal bipartite consensus control (OBCC) scheme is proposed for heterogeneous multiagent systems (MASs) with input delay by reinforcement learning (RL) algorithm. A directed signed graph is established to construct MASs with competitive and cooperative relationships, and model reduction method is developed to tackle input delay problem. Then, based on the Hamilton–Jacobi–Bellman (HJB) equation, policy iteration method is utilized to design the bipartite consensus controller, which consists of value function and optimal controller. Further, a distributed event-triggered function is proposed to increase control efficiency, which only requires information from its own agent and neighboring agents. Based on the input-to-state stability (ISS) function and Lyapunov function, sufficient conditions for the stability of MASs can be derived. Apart from that, RL algorithm is employed to solve the event-triggered OBCC problem in MASs, where critic neural networks (NNs) and actor NNs estimate value function and control policy, respectively. Finally, simulation results are given to validate the feasibility and efficiency of the proposed algorithm.     

Sorting, selection, and routing on the array with reconfigurableoptical buses

In this paper, we present efficient algorithms for sorting, selection, and packet routing on the AROB (Array with Reconfigurable Optical Buses) model. One of our sorting algorithms sorts n general keys in O(1) time on an AROB of size n^ϵ×n for any constant ϵ>0. We also show that selection from out of n elements can be done in randomized O(1) time employing n processors. Our routing algorithm can route any h-relation in randomized O(h) time. All these algorithms are clearly optimal