Fault estimation and fault tolerant control for discrete-time nonlinear systems with perturbation by a mixed design scheme

This paper focuses on the observer-based fault-tolerant control problem for the discrete-time nonlinear systems with the perturbation and the fault signals. First, the nonlinear term with perturbation is put into the local nonlinear part so that the nonlinear system with perturbation can be described as an interval type-1 (IT1) T-S fuzzy system. Then, based on the unknown input observer technology, the IT1 T-S fuzzy fault estimation (FE) observer scheme is presented to obtain the real-time FE information and decouple the local nonlinear part from the estimation error system, where the design complexity and the computational burden are reduced simultaneously. Second, based on the real-time FE information, an FE-based interval type-2 (IT2) T-S fuzzy fault-tolerant control scheme is presented to achieve the compensation for the influence of the fault signal and the stabilization for the system. Different from the traditional methods, a mixed design scheme, which is based on the IT1 T-S fuzzy fault estimation observer method and the IT2 T-S fuzzy fault-tolerant controller method, is proposed in this paper. This strategy can not only reduce the computational burden, but also obtain a less conservative result. Finally, the effectiveness of the mixed design approach is illustrated by an example.     

传感器网络中一种有效的汇聚节点放置策略

研究了无线传感器网络中的数据汇聚节点放置问题. 通过将无限的搜索空间划分为有限的"通讯交集",在量级上降低了问题的复杂度. 基于这样的理念,建立了基于网格的线性优化模型,并由仿真试验验证了此方案的有效性.     

An efficient simulation of the fractional chaotic system and its synchronization

The computational complexity of the numerical simulation of fractional chaotic system and its synchronization control is O(N²) compared with O(N) for integer chaotic system, where N is step number and O is the computational complexity. In this paper, we propose optimizing methods to solve fractional chaotic systems, including equal-weight memory principle, improved equal-weight memory principle, chaotic combination and fractional chaotic precomputing operator. Numerical examples show that the combination of these algorithms can simulate fractional chaotic system and synchronize the fractional master and slave systems accurately. The presented algorithms for simulation and synchronization of fractional chaotic system are up to 1.82 and 1.75 times faster than the original implementation respectively.     

I-S detectability of partially-observed discrete event systems: a novel matrix-based method

The state estimation problem is always considered to be critical in discrete event systems (DESs). In this paper, two methods are proposed for investigating the initial state estimation problem: one is the matrix-based dimension reduction tracking observation system, and the other is the matrix-based reversal observation system. In our paper, the initial state estimation is treated as the initial-state detectability (I-S detectability). Using the Boolean semi-tensor product method of matrices, the corresponding algebraic forms of the partially-observed DES are separately constructed, where the corresponding computational complexity is reduced to some degree. Based on the newly defined state transition output-event observation matrix, necessary and sufficient criteria are established to determine the I-S detectability of the addressed system. Illustrative examples are also given to show feasibility of the derived results.     

Conjugate gradient-based FLANN algorithms in nonlinear active noise control

The conjugate gradient (CG) method exhibits fast convergence speed than the steepest descent, which has received considerable attention. In this work, we propose two CG-based methods for nonlinear active noise control (NLANC). The proposed filtered-s Bessel CG (FsBCG)-I algorithm implements the functional link artificial neural network (FLANN) as a controller, and it is derived from the Matérn kernel to achieve enhanced performance in various environments. On the basis of the FsBCG-I algorithm, we further develop the FsBCG-II algorithm, which utilizes the Bessel function of the first kind to constrain outliers. As an alternative, the FsBCG-II algorithm has reduced computational complexity and similar performance as compared to the FsBCG-I algorithm. Moreover, the convergence property of the algorithms is analyzed. The proposed algorithms are compared with some highly cited previous works. Extensive simulation results demonstrate that the proposed algorithms can achieve robust performance when the noise source is impulsive, Gaussian, logistic, and time-varying.     

DECISION TREE COMPLEXITY OF GRAPH PROPERTIES

１　Ｉｎｔｒｏｄｕｃｔｉｏｎ　　ＡｇｒａｐｈＧｉｓａｎｏｒｄｅｒｅｄｐａｉｒｏｆｄｉｓｊｏｉｎｔｓｅｔｓ（Ｖ,Ｅ）ｓｕｃｈｔｈａｔＥｉｓａｓｕｂｓｅｔｏｆｔｈｅｓｅｔｏｆｕｎｏｒｄｅｒｅｄｐａｉｒｓｏｆＶ,ｗｈｅｒｅｔｈｅｓｅｔｓＶａｎｄＥａｒｅｆｉｎｉｔｅ．ＴｈｅｓｅｔＶｉｓｃａｌｌｅｄｔｈｅｓｅｔｏｆｖｅｒｔｉｃｅｓａｎｄＥｉｓｃａｌｌｅｄｔｈｅｓｅｔｏｆｅｄｇｅｓ．ＴｈｅｙａｒｅｕｓｕａｌｌｙｄｅｎｏｔｅｄｂｙＶ（Ｇ）ａｎｄＥ（Ｇ）,ｒｅｓｐｅｃｔｉｖｅｌｙ．Ａｎｅｄｇｅ｛ｘ,ｙ｝ｉｓｓａ…     

现代密码学中的素性测试问题

由于现代密码学正是建立在整数分解理论和计算复杂性理论的基础之上,因此素性测试问题对现代密码学的影响引起了人们的关注.本文将主要讨论现代密码学中的素性测试问题及其算法实现.然后再介绍几种素性测试算法及其实现.     

Structure of hierarchic clusterings: implications for information retrieval and for multivariate data analysis

Hierarchic clustering methods may be used to condense information for a user, as they are in multivariate data analysis, or to achieve computational advantages, as they are in information retrieval. The structure of the hierarchic classification produced has a direct bearing on the effectiveness and utility of using cluster analysis, yet this important feature of the classification has only been implicitly referred to in the literature to date. In this study, three different coefficients are defined, each of which quantify the symmetry-asymmetry (balancedness-unbalancedness) of hierarchic clusterings on a scale from 0 to 1. Using examples of data from the areas of information retrieval and of multivariate data analysis, a number of hierarchic clustering methods are discussed in terms of the hierarchies they produce.     

Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes

Fault detection and diagnosis is crucial in recent industry sector to ensure safety and reliability, and improve the overall equipment efficiency. Moreover, fault detection and diagnosis based on k-nearest neighbor rule (FDD-kNN) has been effectively applied in industrial processes with characteristics such as multi-mode, non-linearity, and non-Gaussian distributed data. The main challenge associated with FDD-kNN is the on-line computational complexity and storage space that are needed for searching neighbors. To deal with these issues, this paper proposes a monitoring approach where the Fuzzy C-Means clustering technique is used to decrease the overall on-line computations and required storage by measuring the neighbors of the clusters’ centres rather than the raw data. After building the monitoring model off-line based on the data clusters’ centres, the faults are detected by comparing the average squared Euclidean distance between the on-line data sample and the clusters’ centres with a predefined threshold. Then, the detected faults can be diagnosed by calculating the contribution of each variable in the fault detection index. Furthermore, for easily analysing the fault diagnosis results, the relative contribution for each sample data vector is considered. A numerical example and the Tennessee Eastman chemical process are used to demonstrate the performance of the proposed FCM-kNN for fault detection and diagnosis.     

Stability and stabilization for switched positive systems under a weighted MDADT method

The stability and stabilization synthesis problems of the switched positive systems (SPSs) with external disturbances are studied in this paper. For the studied SPSs, a weighted mode-dependent average dwell time (WMDADT) switched strategy has been adopted to analyze the above-mentioned issue, based on which the deficiencies of the existing ADT and MDADT switching techniques can be overcomed. By using the adopted strategy, some improved stability conditions that have less conservativeness are presented for the systems under investigation. Moreover, based on the developed stability conditions, an efficient controller design method avoiding computational complexity and eliminating the rank requirement of the controller is presented. In the end, the effectiveness of the method is verified by two numerical examples.     

The benefits of multivariate singular spectrum analysis over the univariate version

Singular Spectrum Analysis (SSA) is a relatively simple and powerful method in the area of time series analysis that is mainly based on matrix analysis. In this paper, we present a methodological comparison between the univariate and multivariate versions of SSA. Additionally, we explore the advantages of multivariate SSA in terms of theoretical results and with application to a real data set on currency exchange rates.     

A novel intelligent classification model for breast cancer diagnosis

Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are clearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer intelligent diagnosis approach has been proposed, which employed information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future. Moreover our proposed method could also be applied to other illness diagnosis.     

Novel space shift keying MIMO technique based on a Steiner triple system

A novel space shift keying (SSK) multiple–input multiple–output (MIMO) technique based on Steiner triple system is proposed and analyzed in this article. SSK attracted considerable research interest in the past few years driven by the several promised inherent advantages including low error probability, low computational complexity and a very simple hardware implementation with very low cost and power consumption. Yet, the spectral efficiency of SSK increases with a base two logarithm of the number of transmit antennas and high data rates are only viable with a massive and impractical number of transmit antennas. Alternatively, generalized SSK (GSSK) scheme is considered, where a combination of antennas is activated at each time instant. GSSK promises the use of arbitrary number of transmit antennas not necessarily a power of two integer. Also, GSSK can attain high data rate with low number of transmit antennas at the cost of substantial degradation in the error performance. In this study, a Steiner triple system is utilized to propose a tailored SSK scheme with substantial reduction in the required number of transmit antennas, without compromising the error probability. It is shown that the proposed Steiner–SSK (S–SSK) MIMO system achieves almost identical error performance to a conventional SSK system but with nearly 90% reduction in the number of transmit antennas. As well, the average bit error rate (ABER) of S–SSK is shown to outperform GSSK by at least 3dB. It is also reported that a S–SSK system accomplishes significant reduction in hardware cost, power consumption, and computational complexity as compared to conventional SSK scheme. Yet, GSSK is shown to marginally outperforms S–SSK in these metrics as it requires smaller number of transmit antennas per a target spectral efficiency.     

An exact closed-form formula of collision probability in diverse multiple access communication systems with frame slotted aloha protocol

For diverse multiple access communication systems based on frame slotted aloha (FSA) protocol, it is important to analyze collision probability for the system performance evaluation. As shown in the literature, for general settings, it is difficult to derive an exact and closed-form solution for collision probability without approximation. Recently, an exact solution based on generic analytical approach (GAA) [31] has been proposed, yet its numerical computation will become difficult when the number of slots is larger than 16. In this paper, we develop an exact closed-form formula (ECFF) for collision probability that can not only overcome the computational deficiency of GAA in the presence of a large number of slots, but also reduce the computation complexity of collision probability. Surprisingly, by introducing a differentiation operator to form a hybrid recursive equation and applying various algebraic properties of Laplace transform and Z transform, the final collision probability can be represented by a compact double summation. Accuracy of the ECFF and comparison with the GAA have been studied by Monte Carlo simulation.     

Stimulus space complexity determines the ratio of specialist and generalist neurons during pattern recognition

Neural processing layers built on divergent connectivity patterns display two types of stimulus-dependent responses: neurons that react to a few stimuli, specialists, and other ones that respond to a wide range of inputs, generalists. Specialists are essential for the discrimination of stimuli and generalists extract common and generic properties from them. This neural heterogeneity could have emerged because of animal adaptation to the environment. Thus, we suggest that there is a relationship between the percentage of specialists and generalists and the stimulus complexity. In order to study this possible relationship, we use patterns with different complexities in a bio-inspired neural network and calculate their classification errors for different ratios of these types of neurons. This study shows that, when the complexity of the stimuli is low, the minimum classification error is achieved with almost any specialist-generalist ratio. Thus, in this case, the role of these neurons during pattern recognition is unspecific. When this complexity is intermediate, both are needed to minimize the classification error, usually in a similar proportion. For increasing stimulus complexity, the importance of generalists decreases, until their relevance is fully nullified when the complexity is high. Therefore, if we adjust the specialist-generalist ratio to the complexity of patterns, we can build more effective neural networks for pattern recognition. Finally, we propose an estimation of stimulus complexity based on the proportion of these types of neurons observed by neural recordings. This offers the possibility to evaluate the stimulus complexity to which animals are adapted.     

基于创新的服务业分类问题探讨

面对纷杂的服务业,对服务业进行各种各样分类研究的努力一直就没有停止过.但从服务创新的角度进行分类研究,并没有得到公认的结果.事实上,不论是服务业的经济研究,还是服务业的创新研究一直滞后于制造业,在很大程度上归咎于服务业行业的复杂性.开展服务业的类型学研究,是人们更好地认识服务业的必由之路.在简述服务业一般分类方法的基础上,探讨了基于服务创新的分类问题,描述了新的分类原则及其特征.     

Supervised Hebb rule based feature selection for text classification

Text documents usually contain high dimensional non-discriminative (irrelevant and noisy) terms which lead to steep computational costs and poor learning performance of text classification. One of the effective solutions for this problem is feature selection which aims to identify discriminative terms from text data. This paper proposes a method termed “Hebb rule based feature selection (HRFS)”. HRFS is based on supervised Hebb rule and assumes that terms and classes are neurons and select terms under the assumption that a term is discriminative if it keeps “exciting” the corresponding classes. This assumption can be explained as “a term is highly correlated with a class if it is able to keep “exciting” the class according to the original Hebb postulate. Six benchmarking datasets are used to compare HRFS with other seven feature selection methods. Experimental results indicate that HRFS is effective to achieve better performance than the compared methods. HRFS can identify discriminative terms in the view of synapse between neurons. Moreover, HRFS is also efficient because it can be described in the view of matrix operation to decrease complexity of feature selection.     

A family of proportionate normalized subband adaptive filter algorithms

In this paper, the concept of proportionate adaptation is extended to the normalized subband adaptive filter (NSAF), and seven proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are proportionate normalized subband adaptive filter (PNSAF), μ‐law PNSAF (MPNSAF), improved PNSAF (IPNSAF), the improved IPNSAF (IIPNSAF), the set-membership IPNSAF (SM-IPNSAF), the selective partial update IPNSAF (SPU-IPNSAF), and SM-SPU-IPNSAF which are suitable for sparse system identification in network echo cancellation. When the impulse response of the echo path is sparse, the PNSAF has initial faster convergence than NSAF but slows down dramatically after initial convergence. The MPNSAF algorithm has fast convergence speed during the whole adaptation. The IPNSAF algorithm is suitable for both sparse and dispersive impulse responses. The SM-IPNSAF exhibits good performance with significant reduction in the overall computational complexity compared with the ordinary IPNSAF. In SPU-IPNSAF, the filter coefficients are partially updated rather than the entire filter at every adaptation. In SM-SPU-IPNSAF algorithm, the concepts of SM and SPU are combined which leads to a reduction in computational complexity. The simulation results show good performance of the proposed algorithms.     

Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems

As a recent swarm intelligence optimization algorithm, sparrow search algorithm (SSA) is widely adopted in many real-world problems. However, the solutions to the limitations of SSA (such as low accuracy of convergence and tendency of trapping into local optimum) are still not available. To address these issues, we propose an enhanced multi-strategies sparrow search algorithm (EMSSA) based on three strategies specifically addressing the limitations of SSA: 1) in the uniformity-diversification orientation strategy, we propose an adaptive-tent chaos theory to allow more diversity and greater randomness in the initial population; 2) in the hazard-aware transfer strategy, we construct a weighted sine and cosine algorithm based on the growth function to avoid trapping into the state of local optima stagnation; 3) in the dynamic evolutionary strategy, we design the similar perturbation function and introduce the triangle similarity theory to improve the exploration capability. The performance of EMSSA in solving the continuous optimization problems about the 23 benchmark functions, CEC2014, and CEC2017 problems is much improved than that of SSA and other state-of-the-art algorithms. Furthermore, the results of the density peak clustering optimization show that the EMSSA outperforms SSA.     

Some augmented approaches to the improved stability criteria for linear systems with time-varying delays

This work investigates the improved stability conditions for linear systems with time-varying delays via various augmented approaches. Some augmented approaches are augmented Lyapunov-Krasovskii functionals, augmented zero equalities, and the augmented zero equality approach. At first, by constructing augmented Lyapunov-Krasovskii functionals including the state vectors which were not considered in the previous works and augmented zero equalities, a stability criterion is proposed in the forms of linear matrix inequalities. Through the proposed Lyapunov-Krasovskii functionals and an additional functional derived from the integral inequality, a slightly improved result is derived. The proposed results do not consider the increase in the computational complexity to achieve a larger delay bound. So, by applying the augmented zero equality approach, which is a method of grafting the proposed augmented zero equality proposed in Finsler Lemma, to the proposed result, an enhanced stability result was derived. Also, the computational complexity is reduced by appropriately adjusting any vector of the integral inequality utilized in the proposed criteria. By applying some numerical examples to the proposed conditions, the effectiveness and superiority of the results are confirmed.