先验知识与基于核函数的回归方法的融合

In some sample based regression tasks, the observed samples are quite few or not informative enough. As a result, the conflict between the number of samples and the model complexity emerges, and the regression method will confront the dilemma whether to choose a complex model or not. Incorporating the prior knowledge is a potential solution for this dilemma. In this paper, a sort of the prior knowledge is investigated and a novel method to incorporate it into the kernel based regression scheme is proposed. The proposed prior knowledge based kernel regression (PKBKR) method includes two subproblems: representing the prior knowledge in the function space, and combining this representation and the training samples to obtain the regression function. A greedy algorithm for the representing step and a weighted loss function for the incorporation step are proposed. Finally, experiments are performed to validate the proposed PKBKR method, wherein the results show that the proposed method can achieve relatively high regression performance with appropriate model complexity, especially when the number of samples is small or the observation noise is large.     

具有饱和状态反馈离散时滞系统的渐近稳定性

讨论具有饱和状态反馈的离散时滞线性系统的渐近稳定性问题. 给出了在一定假设下, 判断系统全局渐近稳定和局部渐近稳定的充分条件, 并通过数值算例验证了所给出的条件是有效的.     

输入采用广义梯形隶属函数的两维最简模糊控制器结构分析

总结了应用最为广泛的三角形和梯形隶属函数的共同特点, 明确定义了一种将以上两种隶属函数作为特例的广义梯形 (Generalized trapezoid-shaped, GTS) 隶属函数, 推导了输入变量采用 GTS 隶属函数的 I 类和 II 类两维最简模糊控制器的解析式. 基于此, 深入研究了模糊控制器的解析结构, 并证明了这两类模糊控制器等价于一种变结构的非线性 (或线性) PI 控制器与相应的非线性 (或定常) 控制偏置之和, 并且在其输入论域上是单调递增、连续且有界的. 最后, 将该类控制器应用于倒立摆控制系统, 通过仿真证明了其有效性, 同时揭示了此类控制器是一种更一般化的模糊控制器.     

基于模糊Lyapunov函数方法的模糊广义系统H∞控制

研究一类T-S模糊广义系统的容许性条件和H∞控制问题. 首先将原系统表示成增广系统, 进而基于新的模糊Lyapunov函数和模糊控制器得到容许性条件. 所得开环容许条件不要求子系统是容许的; 闭环容许条件中不含有控制输入矩阵与控制增益矩阵的乘积项. 对于H∞控制问题, 利用隶属度函数的性质对H∞控制条件进行了放宽, 并得到了改进的严格线性矩阵不等式(LMI)形式的H∞控制条件. 数值算例表明所得结论较已有文献具有较小的保守性.     

基于特征可分性的核函数自适应构造

核函数的选择与构造是支撑向量机研究中的关键问题和难点.该文针对该问题,首先讨论了特征空间的线性可分性,推导了其判别条件.然后,根据特征完全可分条件,基于函数逼近论和核函数的基本性质,提出了自适应多项式核函数和B-样条核函数模型,给出了模型参数的估计算法.实测数据仿真实验结果表明,与经典的核函数相比,该文提出的算法在分类性能上取得了明显改善.     

18位高精度音频Σ-ΔDAC设计

采用基于过采样Σ-ΔDAC调制技术设计的音频D/A转换器,对量化噪声进行有效整形,提高了分辨率和带内信噪比(SNR)。重点对Sigma-delta设计进行了详细分析,给出了有关电路结构和仿真结果。芯片已在TSMC 0.18μm CMOS工艺上流片成功,在工作频率6.144MHz时动态范围达128.6dB,信噪比109.5dB,总谐波失真达-117.2dB。     

Mathematical model for quality cost optimization

Quality engineering uses robust design in order to improve quality by reducing the effects of variability. Variability of the product can be reduced by two stages. One is parameter design which is adjustable to the nominal value so that output is less sensitive to the cause of variability. Other one is tolerance design which is to reduce the tolerance in order to control variability. All costs incurred in a product life cycle can be divided into two categories—manufacturing cost before the sale to the customer and quality loss after the shipment of the product to the customer. It is very important to find the optimum tolerances for each of the characteristics. A balance between manufacturing cost and quality loss should be arrived at in the tolerance design for quality improvement and cost reduction. For the case of Nominal-The-Best, a mathematical model is developed in order to determine the optimum product tolerance and minimize the total cost which includes the manufacturing cost and the quality loss. Since the process capability index (C_pm) shows the balance of quality responsibility between the design and the manufacturing engineers, this is taken as the basis in developing the functional relationship between the variability of the product and the tolerance. Based on these relationships, the total cost of model can be expressed as a function of product tolerance from which the optimal tolerance limits can be found out. Finally, using this model a tolerance design approach that increases the quality and reduces the cost can be achieved in the early stages of the product process design stage itself.     

Classification of face images using local iterated function systems

There has been an increasing interest in face recognition in recent years. Many recognition methods have been developed so far, some very encouraging. A key remaining issue is the existence of variations in the input face image. Today, methods exist that can handle specific image variations. But we are yet to see methods that can be used more effectively in unconstrained situations. This paper presents a method that can handle partial translation, rotation, or scale variations in the input face image. The principal is to automatically identify objects within images using their partial self-similarities. The paper presents two recognition methods which can be used to recognise objects within images. A face recognition system is then presented that is insensitive to limited translation, rotation, or scale variations in the input face image. The performance of the system is evaluated through four experiments. The results show that the system achieves higher recognition rates than those of a number of existing approaches. The author would like to thank the Australian Research Council (ARC) which supports this research with a Discovery Grant.     

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem.     

不同种类支持向量机算法的比较研究

介绍一种新型的机器学习方法-支持向量机.论述了不同种类支持向量机算法并指出了每种算法的优劣.实验结果显示了核函数中选择合适的参数对分类器的效果是很重要的,通过实验还重点比较了Chunking、SMO和SVMlight三种典型分解算法,并分析了训练速度优劣的原因.文章最后给出了今后SVM研究方向的一些预见.