Multivariable toolbox for use with MATLAB.

The authors describe a toolbox for the frequency-domain analysis and design of multivariable feedback systems, to be used with PC-Matlab, or Pro-Matlab. The principal model representations used by the toolbox are described. Its capabilities are illustrated by a worked design example, which shows the use of a Nyquist array method. Other design techniques supported by the toolbox are briefly reviewed.

Analysis of multivariable Smith predictors using MATLAB Containers

Containers are structured m-files which allow `data' and `methods' to be stored persistently. Containers have a user-defined class structure, so that one can have several Containers of the same class, all structurally similar, and there is a mechanism for interaction with Containers in the style of database transactions. The use of MATLAB Containers to analyze multivariable Smith predictors is discussed.

Manopt, a matlab toolbox for optimization on manifolds

Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on problems where the smooth geometry of the search space can be leveraged to design effcient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network registration, independent component analysis, metric learning, dimensionality reduction and so on. The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms. By dealing internally with most of the differential geometry, the package aims particularly at lowering the entrance barrier. © 2014 Nicolas Boumal.

优先度排序RBF神经网络在与文本无关说话人确认中的应用

该文介绍了优先度排序径向基函数（PORBF）神经网络的结构与算法，并提出了将其应用于与文本无关说话人确认时的训练算法，似然度的计算方法以及识别规则。为了增强PORBF网络的泛化能力，该文用压缩矢量构造抑制样本集，提出了顺序选取、最近邻选取和最远距离选取等3种选择抑制样本集中说话人的方法，并对PORBF神经元的输出进行了等比递减加权。在相同条件下的与文本无关说话人确认实验中，传统的矢量量化方法的等差错率可达10.56%，而基于PORBF网络的确认系统使用最近邻选择方法构造抑制样本集，其等差错率可达6.83%，性能提高很多。

在MATLAB环境下实现的语音识别

介绍了一种基于MATLAB的多个特定人连接词语音识别的方法,并提出了在进行端点检测时,引入平均的概念能进一步提高识别率。此设计是以LPCC系数、DTW算法为核心的基于图形界面的设计。通过大量的实验测试,表明该方法基本达到屏蔽外界环境的影响,具有非常高的精度识别。

线性整数规划的分支限界解法及其MATLAB实现

讨论了计算机算法中分支限界法对于线性整数规划问题的解决思想,通过MATLAB中提供的优化函数,实现了快速解决线性整数规划的问题,同时改进了对于此类问题的计算量巨大和编制程序困难的问题,使用了堆栈而不是递归,节约了计算中的资源。编制的MATLAB线性整数规划的程序,可以编译成C/C++实现和COM组件。

土壤持水曲线vanGenuchten模型求参的Matlab实现

土壤持水曲线是研究土壤水力学性质必不可少的 ,在已经建立的众多数学模型中 ,vanGenuchten模型是目前运用最广泛的模型 ,而运用该模型的关键是其 4个参数的求解。为此 ,本文对同一组东北褐土的土壤水吸力和对应的土壤含水量数据较详细地介绍了Matlab软件的非线性拟合和非线性回归函数的运用 ,得出了该土壤vanGenuchten模型的 4个参数值 ,分别建立了该土壤的vanGenuchten模型 ,并利用Mat lab强大的绘图功能对它们进行了直观比较。最后运用方差分析和残差分析对该模型的计算值与实测数据进行了分析 ,结果表明 :非线性拟合和非线性回归函数求参结果的显著水平均达到p<0 0 0 0 1,残差平方和均小于 0 0 0 0 5 ,其中非线性回归函数的求参结果较非线性拟合好。因此 ,运用Matlab软件的非线性拟合和非线性回归函数对土壤持水曲线的vanGenuchten模型进行求参是切实可行的 ,从而为土壤学工作者寻求出了一条运用数值计算方法的新途径。

Reti complesse e analisi del segnale elettroencefalografico

The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analyzed using a phase synchronization based measure, minimum spanning tree and k-core decomposition. The analysis was performed for each classical brain rhythm separately. Furthermore, we aim to provide a network approach insensitive to the effects that epoch length has on functional connectivity (FC) and network reconstruction. Two different measures, the phase lag index (PLI) and the Amplitude Envelope Correlation (AEC), were applied to EEG resting-state recordings for a group of eighteen healthy volunteers. Weighted clustering coefficient (CCw), weighted characteristic path length (Lw) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Results about distinctive functional core, show highest classification rates from k-core decomposition in gamma (EER=0.130, AUC=0.943) and high beta (EER=0.172, AUC=0.905) frequency bands. Results from scalp analysis concerning the influence of epoch length, show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 seconds for PLI and 6 seconds for AEC. Moreover, CCw and Lw show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 seconds versus 4-8 seconds for AEC). At the source-level the results were even more reliable, with stability already at 1 second duration for PLI-based MSTs. Our results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. Regarding epoch length, the present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.

Interpolación Polinomial usando el Matlab

Con frecuencia se presenta la oportunidad de ajustar curvas a un conjunto de datos representados por puntos. Las técnicas desarrolladas para este fin dependen, fundamentalmente, de la naturaleza de los datos involucrados. El propósito de este trabajo es introducir a los alumnos de Cálculo Numérico, en el uso de la técnica de ajuste de curvas por medio de la interpolación en la solución de problemas de ingeniería, utilizando el paquete MATLAB. Además de que se espera que los alumnos asimilen y dominen los conceptos específicos impartidos referidos a la interpolación, se pretende que comprueben lo indispensable de la utilización de una computadora para resolver este tipo de problemas. También se espera, a partir de las distintas actividades propuestas a realizar por los alumnos, que observen y reconozcan cuándo la interpolación polinomial resulta apropiada arribando así, a resultados satisfactorios. Es decir que en esta primer instancia, se espera que los alumnos hayan aprendido a valorar la confiabilidad de las respuestas y ser capaces de escoger el mejor método (o métodos) para cualquier problema que deben afrontar frecuentemente en la práctica de la ingeniería o en diferentes problemas científicos o tecnológicos. Además, como resultado del análisis y comprensión de las actividades presentadas en este trabajo, se pretende introducir a los alumnos en el uso de la técnica de ajuste de curvas por medio de la regresión, a fin de que comprendan la diferencia entre interpolación y regresión, y que el confundirlos puede llevarlos a resultados erróneos.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.