The automatic model selection and variable kernel width for RBF neural networks

The Orthogonal Least Squares (OLS) algorithm has been extensively used in basis selection for RBF networks, but it is unable to perform model selection automatically because the tolerance ρ must be specified manually. This introduces noise and it is difficult to implement in the parametric complexity of real-time system. Therefore, a generic criterion that detects the optimum number of its basis functions is proposed. In this paper, not only the Bayesian Information Criterion (BIC) method, used for fitness calculation, is incorporated into the basis function selection process of the OLS algorithm for assigning its appropriate number, but also a new method is developed to optimize the widths of the Gaussian functions in order to improve the generalization performance. The augmented algorithm is employed to the Radial Basis Function Neural Networks (RBFNN) for known and unknown noise nonlinear dynamic systems and its performance is compared with the standard OLS; experimental results show that both the efficacy of BIC for fitness calculation and the importance of proper choice of basis function widths are significant.     

Multi-class support vector machine for classification of the ultrasonic images of supraspinatus

This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear. The supraspinatus tendon is often involved in the above-mentioned disease groups. Four different texture analysis methods – texture feature coding method, gray-level co-occurrence matrix, fractal dimension evaluation and texture spectrum – are used to extract features of tissue characteristic in the ultrasonic supraspinatus images. The mutual information criterion is adopted to select the powerful features from ones generated from the above-mentioned four texture analysis methods in the training stage, meanwhile, the five implementations of multi-class support vector machine classifiers are also designed to discriminate each image into one of the four disease groups in the classification stage. In experiments, the most commonly used performance measures including sensitivity, specificity, classification accuracy and false-negative rate are applied to evaluate the classification of the five implantations of multi-class support vector machines. In addition, the receiver operating characteristics analysis is also used to analyze the classification capability. The present results demonstrate that the implementation of multi-class fuzzy support vector machine can achieve 90% classification accuracy, and performance measures of this implementation are significantly superior to the others.     

LabVIEW数据处理中数据剔除功能的应用

在LabVIEW测量程序中嵌入数据剔除功能,可提高测量结果的准确度.通过比较不同的数据剔除准则可知,当检验数据量n较小时(n＜100),采用格拉布斯准则剔除含粗大误差的数据较为合适.根据格拉布斯准则,在LabVIEW中实现数据剔除的功能,并以超声波测距为例,说明数据剔除功能对改善测量结果的作用.     

A neural-network-based exponential H∞ synchronisation for chaotic secure communication via improved genetic algorithm

In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H^∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H^∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.     

Stochastic feedback and its relation to the entropy criterion

In this paper, we generalize the interpretation of the entropy criterion by using stochastic feedback. When a common white Gaussian noise of zero mean and unit variance is applied, the entropy of a transfer function is shown to be equivalent to the maximum variance of the outputs fed back and added to the input through multiplication of a stochastic gain. It turns out that the existing minimum entropy control is useful for minimizing the worst effect of external noises on the outputs when stochastic feedback is applied.     

A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem

In this paper, a discrete artificial bee colony (DABC) algorithm is proposed to solve the lot-streaming flow shop scheduling problem with the criterion of total weighted earliness and tardiness penalties under both the idling and no-idling cases. Unlike the original ABC algorithm, the proposed DABC algorithm represents a food source as a discrete job permutation and applies discrete operators to generate new neighboring food sources for the employed bees, onlookers and scouts. An efficient initialization scheme, which is based on the earliest due date (EDD), the smallest slack time on the last machine (LSL) and the smallest overall slack time (OSL) rules, is presented to construct the initial population with certain quality and diversity. In addition, a self adaptive strategy for generating neighboring food sources based on insert and swap operators is developed to enable the DABC algorithm to work on discrete/combinatorial spaces. Furthermore, a simple but effective local search approach is embedded in the proposed DABC algorithm to enhance the local intensification capability. Through the analysis of experimental results, the highly effective performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.     

A semi-parametric generalization of the Cox proportional hazards regression model: Inference and Applications

The assumption of proportional hazards (PH) fundamental to the Cox PH model sometimes may not hold in practice. In this paper, we propose a generalization of the Cox PH model in terms of the cumulative hazard function taking a form similar to the Cox PH model, with the extension that the baseline cumulative hazard function is raised to a power function. Our model allows for interaction between covariates and the baseline hazard and it also includes, for the two sample problem, the case of two Weibull distributions and two extreme value distributions differing in both scale and shape parameters. The partial likelihood approach can not be applied here to estimate the model parameters. We use the full likelihood approach via a cubic B-spline approximation for the baseline hazard to estimate the model parameters. A semi-automatic procedure for knot selection based on Akaike’s information criterion is developed. We illustrate the applicability of our approach using real-life data.     

一种快速轮对踏面光截曲线图像分割方法

为准确地实现轮对轮缘磨耗的测量,提出了将二维最大相关准则的阈值分割法应用于轮对踏面光截曲线图像的分割.引入量子粒子群优化算法代替穷尽搜索获取阈值向量.通过对实际的踏面光截曲线图象分割表明,该方法是一种实用、有效的踏面光截曲线图像分割法.     

Limiting optimal discounted-cost control of a class of time-varying stochastic systems

We consider a class of time-varying -valued control models, and with possibly unbounded costs. The processes evolve according to the system equation x_n+1=G_n(x_n,a_n)+ξ_n ( ), where {ξ_n} are i.i.d. random vectors and {G_n} a sequence of known functions converging to some function G_∞. Under suitable hypotheses, we show the existence of an α-discount optimal policy for the limiting system x_n+1=G_∞(x_n,a_n)+ξ_n.     

网络时代信息安全对策研究

随着信息全球化的发展,公民个人所拥有的信息被他人或非政府组织不正当利用甚至非法窃取的危险性越来越高。信息安全将会成为一个国家亟待解决的问题。本文分析了网络时代导致信息安全隐患重重的因素,提出了网络时代一些保障信息安全的切实可行的措施。