自适应人工免疫算法在数据挖掘中的应用

免疫聚类算法中网络刺激与抑制阈值参数决定了聚类精度和网络的可伸缩性,现有的免疫聚类算法中这些阈值选择采取定值策略,根据问题的特性和操作者的经验确定,算法的泛化能力较差。提出了一种自适应免疫聚类算法,阈值从动态变化的网络结构特征中获取,在网络进化过程中,阈值始终跟随网络内在结构变化而自适应调整,因而获得的最终网络结构更符合原始数据的内在特性,并很好地解决了算法对问题的依赖性问题,提高了算法的泛化能力。仿真实验表明了该算法的有效性。     

考虑数据量化的改进无模型自适应迭代学习控制算法

针对一类存在数据量化的离散时间单输入单输出非线性系统,提出一种带有编码解码量化机制的无模型自适应迭代学习控制(MFAILC)算法.首先使用伪偏导数将受控非线性系统动态线性化,进而考虑系统输出数据经由均匀量化器进行量化处理的过程,并设计了一种编码解码量化机制,最后基于这种编码解码量化机制提出了一种改进的MFAILC算法.理论上给出了算法的收敛性分析,结果表明,当系统存在数据量化时,所提出的算法仍可保证系统收敛.与已有算法相比,所提算法仅利用较少的输入输出数据,就可以实现跟踪误差的零收敛.仿真进一步验证了算法的有效性.     

An adaptive call admission algorithm for cellular networks

In this paper, we first propose a new continuous action-set learning automaton and theoretically study its convergence properties and show that it converges to the optimal action. Then we give an adaptive and autonomous call admission algorithm for cellular mobile networks, which uses the proposed learning automaton to minimize the blocking probability of the new calls subject to the constraint on the dropping probability of the handoff calls. The simulation results show that the performance of the proposed algorithm is close to the performance of the limited fractional guard channel algorithm for which we need to know all the traffic parameters in advance.     

An adaptive training algorithm for back propagation networks

The effect of the coefficients used in the conventional back propagation algorithm on training connectionist models is discussed, using a vowel recognition task in speech processing as an example. Some weaknesses of the use of fixed coefficients are described and an adaptive algorithm using variable coefficients is presented. This is found to be efficient and robust in comparison with the fixed parameter case, to give fast near optimal training and to avoid trial and error choice of fixed coefficients. It has also been successfully used in a vision processing application.     

利用数据挖掘技术改进TCP CUBIC拥塞控制算法

TCP CUBIC拥塞控制算法无法主动判断拥塞和预测丢包。为解决这一难题,算法首先采集网络节点数据,利用多维关联挖掘方法建立模型,引用RTT最大值和最小值变化率判定网络拥塞。其次,划分拥塞等级,利用动态神经网络算法训练模型,确定拥塞窗口的立方增长因子λ和线性增长参数S数值大小,制定不同策略主动调整拥塞窗口增长速度;利用贝叶斯学习推理策略,动态学习预测连接路径丢包概率。最后,结果表明改进算法能有效调整拥塞和预测丢包。     

An adaptive rule-based classifier for mining big biological data

In this paper, we introduce a new adaptive rule-based classifier for multi-class classification of biological data, where several problems of classifying biological data are addressed: overfitting, noisy instances and class-imbalance data. It is well known that rules are interesting way for representing data in a human interpretable way. The proposed rule-based classifier combines the random subspace and boosting approaches with ensemble of decision trees to construct a set of classification rules without involving global optimisation. The classifier considers random subspace approach to avoid overfitting, boosting approach for classifying noisy instances and ensemble of decision trees to deal with class-imbalance problem. The classifier uses two popular classification techniques: decision tree and k-nearest-neighbor algorithms. Decision trees are used for evolving classification rules from the training data, while k-nearest-neighbor is used for analysing the misclassified instances and removing vagueness between the contradictory rules. It considers a series of k iterations to develop a set of classification rules from the training data and pays more attention to the misclassified instances in the next iteration by giving it a boosting flavour. This paper particularly focuses to come up with an optimal ensemble classifier that will help for improving the prediction accuracy of DNA variant identification and classification task. The performance of proposed classifier is tested with compared to well-approved existing machine learning and data mining algorithms on genomic data (148 Exome data sets) of Brugada syndrome and 10 real benchmark life sciences data sets from the UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed classifier has exemplary classification accuracy on different types of biological data. Overall, the proposed classifier offers good prediction accuracy to new DNA variants classification where noisy and misclassified variants are optimised to increase test performance.     

能量感知的WSN自适应数据融合路由算法*

针对传感器节点以能量有效的方式收集相关性数据问题,提出了一种能量感知的自适应数据融合路由算法EAAF(energy-aware adaptive data fusion routing algorithm for wireless sensor networks)。算法选择路由时,根据数据传输和数据融合能量开销及节能增益,对数据迁移到每个传感器节点是否进行数据融合作自适应选择,从而实现在信息收集过程中提高网络的能效。仿真结果表明,能量感知的自适应数据融合路由算法的能效大幅度优于SPT、MST和SLT算法     

An algorithm for multirate sampling adaptive control

A stable multirate sampling adaptive control algorithm is presented which enables the fast sampling rate to be applied in closed-loop feedback control. The sampling rate is only restricted by the time needed for the control law computation (as in the nonadaptive control case) plus the estimation error computation. The plant input and output are recorded prior to the currently obtained estimate and used to compute the coming estimate and controller coefficients. Thus, the computation is not dependent upon the inputs and outputs appearing during the updating process. The closed-loop system is shown to be stable     

An adaptive control algorithm for linear systems

A modified gradient procedure is presented for adjusting parameters in a linear control system in the absence of complete knowledge of the plant dynamic characteristics. The algorithm operates to make discrete-time changes in the adjustable parameters during the normal course of system operation and incorporates the best available information on the unknown quantities. Sufficient conditions for the error corrective properties of the algorithm are derived, and the results of a simulation study are discussed.     

An adaptive scheme for admission control in ATM networks

This paper presents a real time front-end admission control scheme for ATM networks. A call management scheme which uses the burstiness associated with traffic sources in a heterogeneous ATM environment to effect dynamic assignment of bandwidth is presented. In the proposed scheme, call acceptance is based on an on-line evaluation of the upper bound on cell loss probability which is derived from the estimated distribution of the number of calls arriving. Using this scheme, the negotiated quality of service will be assured when there is no estimation error. The control mechanism is effective when the number of calls is large, and tolerates loose bandwidth enforcement and loose policing control. The proposed approach is very effective in the connection oriented transport of ATM networks where the decision to admit new traffic is based on the a priori knowledge of the state of the route taken by the traffic.     

An adaptive control algorithm for robotic deburring

This paper presents the topic of adaptive control for robotic deburring by presenting some nonreal-time and some real-time schemes found in literature. Thereafter, a real-time adaptive control algorithm for robotic deburring is presented. Experiments are presented in which an IBM RS/1 robot is used to simulate the operation of the algorithm.     

机会网络数据收集中的转发控制

针对机会网络中由于节点移动、网络稀疏等各种原因通常导致网络拓扑动态变化大,消息源节点到汇聚节点之间往往不存在稳定的端到端的通信链路,提出了一种基于偏好顺序决策法(the technique for order preference by similarity to ideal so-lution,TOPSIS)的数据收集策略(data gathering based on the TOPSIS,DGT)。DGT策略根据节点的剩余能量、感知节点到汇聚节点的距离以及传感器节点的连通变化,采用TOPSIS评估选择下一跳中继节点。仿真实验表明,与现有的几种典型转发控制机制相比,DGT策略在保证较低传输延迟和较高传输成功率的基础上,通过减少节点间的转发次数,降低了网络传输开销。     

An adaptive ensemble classifier for mining concept drifting data streams

It is challenging to use traditional data mining techniques to deal with real-time data stream classifications. Existing mining classifiers need to be updated frequently to adapt to the changes in data streams. To address this issue, in this paper we propose an adaptive ensemble approach for classification and novel class detection in concept drifting data streams. The proposed approach uses traditional mining classifiers and updates the ensemble model automatically so that it represents the most recent concepts in data streams. For novel class detection we consider the idea that data points belonging to the same class should be closer to each other and should be far apart from the data points belonging to other classes. If a data point is well separated from the existing data clusters, it is identified as a novel class instance. We tested the performance of this proposed stream classification model against that of existing mining algorithms using real benchmark datasets from UCI (University of California, Irvine) machine learning repository. The experimental results prove that our approach shows great flexibility and robustness in novel class detection in concept drifting and outperforms traditional classification models in challenging real-life data stream applications.     

RETRACTED ARTICLE: An effective stadium monitoring control algorithm based on big data in emerging mobile networks

Multimedia Tools and Applications - In the process of monitoring the gymnasium by the traditional radio frequency technology, the parallel computing problem of the large data environment in the...     

LLACA: An adaptive localized clustering algorithm for wireless ad hoc networks

Performance of ad hoc networks dramatically declines as network grows. Cluster formation in which the network hosts are hierarchically partitioned into several autonomous non-overlapping groups, based on proximity, is a promising approach to alleviate the scalability problem of ad hoc networks. In this paper, we propose a localized learning automata-based clustering algorithm for wireless ad hoc networks. The proposed clustering method is a fully distributed algorithm in which each host chooses its cluster-head based solely on local information received from neighboring hosts. The proposed algorithm can be independently localized at each host. This results in a significantly reduction in message overhead of algorithm, and allows cluster maintenance can be locally performed only where it is required. To show the performance of proposed algorithm, obtained results are compared with those of several existing clustering methods in terms of the number of clusters, control message overhead, clustering time, and load standard deviation.     

自适应小生境遗传算法在关联规则挖掘中的应用

传统的遗传算法存在早熟收敛和易于陷入局部搜索最优等缺陷;根据关联规则挖掘的要求和特点,提出一种应用于关联规则挖掘的自适应小生境遗传算法.     

An efficient algorithm for community mining with overlap in social networks

Detecting communities in social networks represents a significant task in understanding the structures and functions of networks. Several methods are developed to detect disjoint partitions. However, in real graphs vertices are often shared between communities, hence the notion of overlap. The study of this case has attracted, recently, an increasing attention and many algorithms have been designed to solve it. In this paper, we propose an overlapping communities detecting algorithm called DOCNet (Detecting overlapping communities in Networks). The main strategy of this algorithm is to find an initial core and add suitable nodes to expand it until a stopping criterion is met. Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that DOCNet is efficient and highly reliable for detecting overlapping groups, compared with four newly known proposals.     

An adaptive algorithm for control of combustion instability

We propose an adaptive algorithm for control of combustion instability suitable for reduction of acoustic pressure oscillations in gas turbine engines, and main burners and augmentors of jet engines over a large range of operating conditions, and supply an experimental demonstration of oscillation attenuation, the first for a large industrial-scale gas turbine combustor. The algorithm consists of an Extended Kalman Filter based frequency tracking observer to determine the in-phase component, the quadrature component, and the magnitude of the acoustic mode of interest, and a phase shifting controller actuating fuel-flow, with the controller phase tuned using extremum-seeking. The paper also identifies a closed-loop model with phase-shifting control of combustion instability from experimental data; supplies stability analysis of the adaptive scheme based upon the identified model, and stable extremum-seeking designs used in experiments.     

An adaptive backoff algorithm for multi-channel CSMA in wireless sensor networks

Multiple channels have been widely used in wireless sensor networks (WSNs) for the improvement of network performance. Since the existing backoff algorithms proposed for single-channel MAC protocols are not suitable for multi-channel carrier sense multiple access (CSMA), we propose an ADaptive Backoff Algorithm (ADBA) for multi-channel CSMA in WSNs, which is able to improve energy efficiency, throughput, and fairness of random channel accesses. A novel feature of ADBA is the adaptability to traffic load, where every node tunes its backoff interval based on the estimation of real-time traffic load in WSNs. A near-optimal backoff interval can be generated using the number of competing nodes on one channel which can be estimated by the channel traffic load. Theoretical analysis indicates that ADBA can generate near-optimal backoff intervals that can maximize energy efficiency and throughput and improve fairness of random channel accesses, compared with other backoff schemes.     

一种太阳能传感器网络的自适应休眠调度算法

通过从外界获取太阳能,传感器网络节点的能量限制得到缓解。提出一种太阳能传感器网络的自适应休眠调度算法。当观测场景从区域观测到目标跟踪转变时,节点自适应地转换活跃和休眠状态,同时剩余能量低于能量阈值的休眠节点从外界获取太阳能。与不考虑太阳能获取的休眠调度算法相比,提出的算法延长了网络生命周期。