Stochastic optimization for the calculation of the time dependency of the physiological demand during exercise and recovery

The stochastic optimization method ALOPEX IV is successfully applied to the problem of estimating the time dependency of the physiological demand in response to exercise. This is a fundamental and unsolved problem in the area of exercise physiology, where the lack of appropriate tools and techniques forces the assumption and the use of a constant demand during exercise. By the use of an appropriate partition of the physiological time series and by means of stochastic optimization, the time dependency of the physiological demand during heavy intensity exercise and its subsequent recovery is, for the first time, revealed.     

Stochastic optimization for the calculation of the optimal critical curve from experimental data in a model of the process of regaining balance after perturbation from quiet stance

We demonstrate the successful application of ALOPEX stochastic optimization to the problem of calculating the optimal critical curve in a dynamical systems model of the process of regaining balance after perturbation from quiet stance. Experimental data provide the time series of angles for which the subjects were able to regain balance after an initial perturbation. The optimal critical curve encloses all data points and has a minimum distance from the border points of the data set. We demonstrate the results of the optimization firstly using the traditional cost function of chi-square distance. We then successfully introduce a modified cost function that fits the model to the experimental data by taking into account the specific requirements of the model. By use of the proposed cost function, combined with the efficiency of our optimization method, an optimal critical curve is calculated even in the cases of very asymmetric data sets that lie within the capabilities of the existing model.     

Stochastic optimization for the detection of changes in maternal heart rate kinetics during pregnancy

The stochastic optimization method ALOPEX IV has been successfully applied to the problem of detecting possible changes in the maternal heart rate kinetics during pregnancy. For this reason, maternal heart rate data were recorded before, during and after gestation, during sessions of exercises of constant mild intensity; ALOPEX IV stochastic optimization was used to calculate the parameter values that optimally fit a dynamical systems model to the experimental data. The results not only demonstrate the effectiveness of ALOPEX IV stochastic optimization, but also have important implications in the area of exercise physiology, as they reveal important changes in the maternal cardiovascular dynamics, as a result of pregnancy.     

Stochastic optimization for a tip-tilt adaptive correcting system

We present computer simulations of a tip-tilt adaptive optics system, where stochastic optimization is applied to the problem of dynamic compensation of atmospheric turbulence. The system uses a simple measure of the light intensity that passes through a mask and is recorded on the image plane, to generate signals for the tip-tilt mirror. A feedback system rotates the mirror adaptively and in phase with the rapidly changing atmospheric conditions. Computer simulations and a series of numerical experiments investigate the implementation of the method in the presence of drifting atmosphere. In particular, the study examines the system's sensitivity to the rate of change of the atmospheric conditions and investigates the optimal size of the mirror's masking area and the algorithm's optimal degree of stochasticity.     

Vector spherical harmonics application to 3-D tomography problem

A method of series expansion with the aid of vector spherical harmonics intended for inverting line integrated data is proposed to investigate 3-D vector fields in the spherical plasmas. A set of numerical computations demonstrating the 3-D reconstruction of the model vector fields has been performed to assess the inversion method proposed.     

A novel model-based hearing compensation design using a gradient-free optimization method

We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal of learning the Neurocompensator parameters, we use a gradient-free optimization procedure, an improved version of the ALOPEX that we have developed, to learn the unknown parameters of the Neurocompensator. We present our methodology, learning procedure, and experimental results in detail; discussion is also given regarding the unsupervised learning and optimization methods.     

Buffer allocation optimization in ATM switching networks using ALOPEX algorithm

We describe the use of a stochastic algorithm, called ALOPEX, which could be implemented in VLSI for optimizing the buffer allocation process in ATM switching networks. We present the results of computer simulations for buffer allocation in ATM switching networks using the ALOPEX algorithm. The algorithm uses a scalar cost function which is a measure of global performance. The ALOPEX works by broadcasting the global cost function to all neural processors in the neural network. Since each neural processor solely depends on the global cost function no interaction is needed between the neural processors and the algorithm is more amenable to massively parallel implementation. The application of the ALOPEX algorithm for the buffer allocation optimization in ATM networks assumes limited buffer capacity. The proposed ALOPEX-based approach takes advantage of the favorable control characteristics of the algorithm such as high adaptability and high speed collective computing power for effective buffer utilization. The proposed model uses complete sharing buffer allocation strategy and enhances its performance for high traffic loads by regulating the buffer allocation process dynamically.     

Stochastic optimization for real-time operation of alumina blending process

In this paper, the stochastic optimization blending operation is applied to the alumina production in this paper. A new binomial distribution based stochastic scenario optimization used together with the sample selection approach is utilized to design the optimal set point for control, under which the probability of quality indices of the raw slurry being within the tolerance region is high enough in the presence of uncertainties caused by fluctuation of the raw material and disturbances. Through practical industrial experiments, it is observed that the proposed stochastic optimization method is effective and the computational cost is low.     

流程工业配料过程的随机优化模型及求解算法

针对配料过程原料质量参数存在的不确定性,以原料消耗成本最小为优化目标,将不确定质量参数以随机数的形式引入质量指标约束中,建立了一种配料过程随机优化模型．考虑传统蒙特卡洛抽样方法的不足,采用一种更高效的Hammersley sequence sampling（HSS）技术,获得随机优化模型对应的期望值优化模型．将HSS技术用于遗传算法的种群初始化和交叉、变异操作,以保证种群分布的均匀性,实现随机优化问题的有效求解．工业应用实验结果表明,所提方法不仅能够有效降低原料的消耗成本,而且能够保证产品质量指标满足生产要求,优化结果具有较好的鲁棒性,为配料过程的随机优化控制提供了一个优化模式．     

The optimal stochastic control of an epitaxial process unit

The problem of computerized batch control of the silicon epitaxial layer deposition technological process has been solved using optimal stochastic control methods. A control algorithm is presented the main emphasis being given to the forecasting and compensating of disturbing processes which act on a process unit under real operation conditions. The method of multidimensional time series, stochastic model form identification for the process noise is developed based on multidimensional time series, correlation analysis results. The “maximum likelihood” identification method is applied in order to obtain efficient estimates of the model parameters. The identification of the model form and model parameters is carried out on the basis of a rather extensive set of data obtained from operation records of a high capacity epitaxial unit. The adequacy of the identified models is checked by means of a correlation analysis of the model residuals. It is demonstrated that results comparable to those with an intuitive process control by an experienced operator, can be achieved when using the algorithm presented in the present work for process computer control. This algorithm thus represents a reliable and rational basis for process control computer software development.     

Real time recursive prediction of river flows

A method is presented for predicting flows in a river. A stochastic model with a set of undetermined parameters is postulated, and these parameters are estimated in real time using the available flow data, and the model is used for prediction. The predictor is recursive and yields the predicted values of flows at a future time instant based upon all presently available information.In addition, a method is given for comparing optimal predictors obtained from different stochastic models. We use the prediction schemes to obtain one-day-ahead forecasts of the Wabash River in the U.S.A. by using the daily riverflow data, paying particular attention to the choice of the order of the stochastic difference equation.     

A novel numerical approach to heterojunction bipolar transistors circuit simulation

In this paper we present a novel computational method for calculating the heterojunction bipolar transistor (HBT) physical characteristics in the time domain. To calculate the HBT high frequency properties, the Gummel-Poon equivalent circuit model is applied to replace the HBT in the circuit and a set of governing ordinary differential equations (ODEs) is formulated. We directly decouple the system ODEs and solve each decoupled ODE with the monotone iterative method in the time domain. This solution methodology proposed here has been applied to semiconductor device simulation by us earlier, and we find this method for the HBT simulation has good accuracy and converges globally. Compared with the HSPICE circuit simulator results, our results present the accuracy, efficiency, and robustness of the method.     

AFMM: A molecular mechanics force field vibrational parametrization program

AFMM (Automated Frequency Matching Method) is a program package for molecular mechanics force field parametrization. The method used fits the molecular mechanics potential function to both vibrational frequencies and eigenvector projections derived from quantum chemical calculations. The program optimizes an initial parameter set (either pre-existing or using chemically-reasonable estimation) by iteratively changing them until the optimal fit with the reference set is obtained. By implementing a Monte Carlo-like algorithm to vary the parameters, the tedious task of manual parametrization is replaced by an efficient automated procedure. The program is best suited for optimization of small rigid molecules in a well-defined energy minimum, for which the harmonic approximation to the energy surface is appropriate for describing the intra-molecular degrees of freedom.

Program summary

Title of program: AFMMCatalogue identifier: ADUZProgram summary URL:http://cpc.cs.qub.ac.uk/summaries/ADUZProgram obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandComputer: x86 PC, SGI, Sun MicrosystemsOperating system: GNU/Linux, BSD, IRIX, SolarisProgramming language used: PythonMemory required: 10 MbytesNo. of bits in a word: 32 or 64No. of processors used: 1Parallelized?: NoNo. of lines in distributed program, including test data, etc.:13 127No. of bytes in distributed program, including test data, etc.: 182 550Distribution format: tar.gzTypical running time: 24 hNature of the physical problem: Molecular mechanics force field parametrization.Method of solution:Fitting of the molecular mechanics potential to normal modes derived from quantum chemical calculations. The missing force field parameters are optimized via a merit function to obtain the optimal fit with the reference quantum mechanical set.     

The trigonometric regressive spectral analysis--a method for mapping of beat-to-beat recorded cardiovascular parameters on to frequency domain in comparison with Fourier transformation

Heart rate and peripheral blood pressure as physiological recorded vegetative parameters are very often rhythmically investigated with the Fourier Transformation (FT). In contrast to the original use of FT these parameters are still stochastic with overlaying rhythmical structures. The R-R intervals as independent variables of time are not equidistant. The mathematical structure for the spectral decomposition is critically analysed. The purpose of this article is the presentation of a mathematical method, considering both the statistical and rhythmical features of such time series. On the basis of trigonometric regressions, this method is presented to eliminate the equidistance problems, arising with the usage of FT, by a new mathematical approach. This method computes more precisely the spectral power especially in the VLF range (0.003-0.04 Hz) than FT, because this method of trigonometric regression does not perform a frequency quantization. This method has been used and successfully tested for the analysis of peripheral blood pressure and R-R intervals including an effective reduction of input data.     

Parallel Optimization of Sparse Portfolios with AR-HMMs

In this paper we optimize mean reverting portfolios subject to cardinality constraints. First, the parameters of the corresponding Ornstein–Uhlenbeck (OU) process are estimated by auto-regressive Hidden Markov Models (AR-HMM), in order to capture the underlying characteristics of the financial time series. Portfolio optimization is then performed by maximizing the return achieved with a predefined probability instead of optimizing the predictability parameter, which provides more profitable portfolios. The selection of the optimal portfolio according to the goal function is carried out by stochastic search algorithms. The presented solutions satisfy the cardinality constraint in terms of providing a sparse portfolios which minimize the transaction costs (and, as a result, maximize the interpretability of the results). In order to use the method for high frequency trading (HFT) we utilize a massively parallel GPGPU architecture. Both the portfolio optimization and the model identification algorithms are successfully tailored to be running on GPGPU to meet the challenges of efficient software implementation and fast execution time. The performance of the new method has been extensively tested both on historical daily and intraday FOREX data and on artificially generated data series. The results demonstrate that a good average return can be achieved by the proposed trading algorithm in realistic scenarios. The speed profiling has proven that GPGPU is capable of HFT, achieving high-throughput real-time performance.     

基于罚函数内点法的泄露积分型回声状态网的参数优化

为了提升泄露积分型回声状态网（Leaky integrator echo state network,Leaky-ESN）的性能,提出利用罚函数内点法优化Leaky-ESN的全局参数,如泄漏率、内部连接权矩阵谱半径、输入比例因子等,这克服了通过反复试验法选取参数值而降低了Leaky-ESN模型的优越性和性能.Leaky-ESN的全局参数必须保障回声状态网满足回声状态特性,因此它们之间存在不等式约束条件.有学者提出利用随机梯度下降法来优化内部连接权矩阵谱半径、输入比例因子、泄露率三个全局参数,一定程度上提高了Leaky-ESN的逼近精度.然而,随机梯度下降法是解决无约束优化问题的基本算法,在利用随机梯度下降法优化参数时,没有考虑参数必须满足回声特性的约束条件（不等式约束条件）,致使得到的参数值不是最优解.由于罚函数内点法可以求解具有不等式约束的最优化问题,应用范围广,收敛速度较快,具有很强的全局寻优能力.因此,本文提出利用罚函数内点法优化Leaky-ESN的全局参数,并以时间序列预测为例,检验优化后的Leaky-ESN的预测性能,仿真结果表明了本文提出方法的有效性.     

Chaotic Bayesian optimal prediction method and its application in hydrological time series

The embedding dimension and the number of nearest neighbors are very important parameters in the prediction of chaotic time series. To reduce the prediction errors and the uncertainties in the determination of the above parameters, a new chaos Bayesian optimal prediction method (CBOPM) is proposed by choosing optimal parameters in the local linear prediction method (LLPM) and improving the prediction accuracy with Bayesian theory. In the new method, the embedding dimension and the number of nearest neighbors are combined as a parameter set. The optimal parameters are selected by mean relative error (MRE) and correlation coefficient (CC) indices according to optimization criteria. Real hydrological time series are taken to examine the new method. The prediction results indicate that CBOPM can choose the optimal parameters adaptively in the prediction process. Compared with several LLPM models, the CBOPM has higher prediction accuracy in predicting hydrological time series.     

Optimal Deterministic and Stochastic Macroeconomic Policies for Slovenia: An Application of the OPTCON Algorithm

This paper reports about applications of optimal control theory to the analysis of macroeconomic policies for Slovenia during its way into the Euro Area. For this purpose, the model SLOPOL4, a macroeconometric model for Slovenia, is used. Optimal policies are calculated using the OPTCON algorithm, an algorithm for determining (approximately) optimal solutions to deterministic and stochastic control problems. We determine optimal exchange rate and fiscal policies for Slovenia as solutions to optimum control problems with a quadratic objective function and the model SLOPOL4 as constraint. Several optimization experiments under different assumptions about the exchange rate regime are carried out. The sensitivity of the results with respect to several assumptions is investigated; in particular, the reaction of the optimal paths on varying the stochastic character of the model parameters is examined. If the stochastic nature of more parameters is taken into consideration, the resulting policies are closer to the deterministic solution than with only a few stochastic parameters.     

The ensemble approach to neural-network learning and generalization

A method is suggested for learning and generalization with a general one-hidden layer feedforward neural network. This scheme encompasses the use of a linear combination of heterogeneous nodes having randomly prescribed parameter values. The learning of the parameters is realized through adaptive stochastic optimization using a generalization data set. The learning of the linear coefficients in the linear combination of nodes is achieved with a linear regression method using data from the training set. One node is learned at a time. The method allows for choosing the proper number of net nodes, and is computationally efficient. The method was tested on mathematical examples and real problems from materials science and technology.     

State Dependent Parameter metamodelling and sensitivity analysis

In this paper we propose a general framework to deal with model approximation and analysis. We present a unified procedure which exploits sampling, screening and model approximation techniques in order to optimally fulfill basic requirements in terms of general applicability and flexibility, efficiency of estimation and simplicity of implementation. The sampling procedure applies Sobol' quasi-Monte Carlo sequences, which display optimal characteristics when linked to a screening procedure, such as the elementary effect test. The latter method is used to reduce the dimensionality of the problem and allows for a preliminary sorting of the factors in terms of their relative importance. Then we apply State Dependent Parameter (SDP) modelling (a model estimation approach, based on recursive filtering and smoothing estimation) to build an approximation of the computational model under analysis and to estimate the variance based sensitivity indices. The method is conceptually simple and very efficient, leading to a significant reduction in the cost of the analysis. All measures of interest are computed using a single set of quasi-Monte Carlo runs. The approach is flexible because, in principle, it can be applied with any available type of Monte Carlo sample.