Chaos control of performance measure approach for evaluation of probabilistic constraints

Performance measure approach (PMA) is a recently proposed method for evaluation of probabilistic constraints in reliability-based design optimization of structure. The advanced mean-value (AMV) method is well suitable for PMA due to its simplicity and efficiency. However, when the AMV iterative scheme is applied to search for the minimum performance target point for some nonlinear performance functions, the iterative sequences could fall into the periodic oscillation and even chaos. In the present paper, the phenomena of numerical instabilities of AMV iterative solutions are illustrated firstly. And the chaotic dynamics analysis on the iterative procedure of AMV method is performed. Then, the stability transformation method of chaos feedback control is suggested for the convergence control of AMV procedure in the parameter interval in which the iterative scheme fails. Numerical results of several nonlinear performance functions demonstrate that the control of periodic oscillation, bifurcation and chaos for AMV iterative procedure is achieved, and the stable convergence solutions are obtained.     

A probabilistic model of the control of technical systems

A model is proposed that describes the consideration of the control of the reliability in renewable systems such that the consideration is applied to determine the moment a system’s failure happens and its repair should take place. The presented model differs from the known models in the fact that the magnitude of the probability of the failure detection is multiplied by the magnitude of the leading function among the functions of the repair probability. The leading function is called the repair resource. Thus, the magnitude of the probability of the correct control determines the magnitude of the repair resource. This means that it also controls the magnitude of the regeneration period for a random process of the repaired system.     

A probabilistic reasoning model: Formulation and control strategy

It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human reasoning processes has become an important subject of research in recent years. In many applications, the reasoning process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the reasoning process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques.     

A probabilistic model for interactive decision-making

A probabilistic reasoning model is defined where the decision maker (d.m.) is engaged in a sequential information-gathering process facing the trade-off between the reliability of the achieved solution and the associated observation cost. The d.m. is directly involved in the proposed flexible control strategy, which is based on information-theoretic principles. The devised strategy works on a Bayesian belief network that allows the efficient representation and manipulation of the knowledge base relevant to the problem domain. It is shown that this strategy guarantees a constant factor approximate solution with respect to the optimum of the decision problem. Some application examples are also discussed.     

System identification for generic model control

System identification methods build mathematical models of dynamical systems based on observed data. The intended use of the model should always be reflected in the methods and techniques used for identification. In this paper an identification scheme is derived for the case where the model is going to be used for GMC controller design. The aim of GMC control is to make the output approach a setpoint along a given desired trajectory. This is reflected in the identification scheme which is non-standard in two ways. Firstly, the emphasis is on the output trajectories of the models, and secondly we try to make the prediction errors follow an error trajectory determined by the controller parameters. Simulation studies are included which show that the derived identification scheme performs well.     

A Petri net model for probabilistic logic

In this article we investigate the use of Petri nets for the representation of possible worlds in probabilistic logic. We propose a method to generate possible worlds based upon the reachability tree of the Petri net model. The number of columns in the matrix of possible worlds grows exponentially with the problem size. Nilsson [Proceedings of 1976 National Computer Conference and Readings in Artificial Intelligence, Morgan Kaufmann, Los Altos, CA, 1981, pp. 192-199] suggested a method to generate only those columns of the possible world matrix that are likely to be important for the solution. We provide Petri net models for the method suggested by Nilsson and show that they lead to intuitive and simple computational methods. © 1996 John Wiley & Sons, Inc.     

A local-motion-based probabilistic model for visual tracking

Color-based tracking is prone to failure in situations where visually similar targets are moving in a close proximity or occlude each other. To deal with the ambiguities in the visual information, we propose an additional color-independent visual model based on the target's local motion. This model is calculated from the optical flow induced by the target in consecutive images. By modifying a color-based particle filter to account for the target's local motion, the combined color/local-motion-based tracker is constructed. We compare the combined tracker to a purely color-based tracker on a challenging dataset from hand tracking, surveillance and sports. The experiments show that the proposed local-motion model largely resolves situations when the target is occluded by, or moves in front of, a visually similar object.     

A unified model for probabilistic principal surfaces

Principal curves and surfaces are nonlinear generalizations of principal components and subspaces, respectively. They can provide insightful summary of high-dimensional data not typically attainable by classical linear methods. Solutions to several problems, such as proof of existence and convergence, faced by the original principal curve formulation have been proposed in the past few years. Nevertheless, these solutions are not generally extensible to principal surfaces, the mere computation of which presents a formidable obstacle. Consequently, relatively few studies of principal surfaces are available. We previously (2000) proposed the probabilistic principal surface (PPS) to address a number of issues associated with current principal surface algorithms. PPS uses a manifold oriented covariance noise model, based on the generative topographical mapping (GTM), which can be viewed as a parametric formulation of Kohonen's self-organizing map. Building on the PPS, we introduce a unified covariance model that implements PPS (0<α<1), GTM (α=1), and the manifold-aligned GTM (α>1) by varying the clamping parameter α. Then, we comprehensively evaluate the empirical performance of PPS, GTM, and the manifold-aligned GTM on three popular benchmark data sets. It is shown in two different comparisons that the PPS outperforms the GTM under identical parameter settings. Convergence of the PPS is found to be identical to that of the GTM and the computational overhead incurred by the PPS decreases to 40 percent or less for more complex manifolds. These results show that the generalized PPS provides a flexible and effective way of obtaining principal surfaces     

1种蛋白质Loop片段结构的概率生成模型

在计算生物学中,根据蛋白质的氨基酸序列预测蛋白质的结构是尚未解决的重要问题之一,而其中的1个难点是预测蛋白质中Loop片段的结构.本文用1阶马尔可夫模型为基础,通过对其训练,可根据氨基酸串和2级结构信息为蛋白质Loop片段概率建模和采样.其中用Ramachandmn图示法的二面角对描述蛋白质结构,模型的训练和推理通过工具包Mocapy来完成.并使用KL交叉熵和角度差异值作为实验检验标准来完成Loop分布情况的测试实验,同时在从头预测Loop结构实验中预测CASP8中8个自由建模的蛋白质结构.与最流行的方法相比,本文提出的模型因为改进了Loop段的预测精度,从而可使得到的二面角对更加接近真实Loop结构中分布,同时在从头预测中提高整个蛋白质结构的预测精度.并且由于本文的模型具有概率推理特性,故在理论上也更具有无偏见性.     

A probabilistic inference model for recommender systems

Recommendation is an important application that is employed on the Web. In this paper, we propose a method for recommending items to a user by extending a probabilistic inference model in information retrieval. We regard the user’s preference as the query, an item as a document, and explicit and implicit factors as index terms. Additional information sources can be added to the probabilistic inference model, particularly belief networks. The proposed method also uses the belief network model to recommend items by combining expert information. Experimental results on real-world data sets show that the proposed method can improve recommendation effectiveness.     

A probabilistic risk analysis for multimodal entry control

Entry control is an important security measure that prevents undesired persons from entering secure areas. The advanced risk analysis presented in this paper makes it possible to distinguish between acceptable and unacceptable entries, based on several entry sensors, such as fingerprint readers, and intelligent methods that learn behavior from previous entries. We have extended the intelligent layer in two ways: first, by adding a meta-learning layer that combines the output of specific intelligent modules, and second, by constructing a Bayesian network to integrate the predictions of the learning and meta-learning modules. The obtained results represent an important improvement in detecting security attacks.     

Resource-bounded measure on probabilistic classes

We extend Lutz's resource-bounded measure to probabilistic classes, and obtain notions of resource-bounded measure on probabilistic complexity classes such as BPE and BPEXP. Unlike former attempts, our resource bounded measure notions satisfy all three basic measure properties, that is every singleton {L} has measure zero, the whole space has measure one, and “enumerable infinite unions” of measure zero sets have measure zero.     

A probabilistic model for predicting software development effort

Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic models, when compared to traditional point forecast estimation models, is that Bayesian models provide tools for risk estimation and allow decision-makers to combine historical data with subjective expert estimates. In this paper, we use a Bayesian network model and illustrate how a belief updating procedure can be used to incorporate decision-making risks. We develop a causal model from the literature and, using a data set of 33 real-world software projects, we illustrate how decision-making risks can be incorporated in the Bayesian networks. We compare the predictive performance of the Bayesian model with popular nonparametric neural-network and regression tree forecasting models and show that the Bayesian model is a competitive model for forecasting software development effort.     

A probabilistic spatial data model

Spatial information in autonomous robot tasks is uncertain due to measurement errors, the dynamic nature of the world, and an incompletely known environment. We present a probabilistic spatial data model capable of describing relevant spatial data, such as object location, shape, composition, and other parameters, in the presence of uncertainty. Uncertain spatial information is modeled through continuous probability distributions on values of attributes. The data model is designed to support our visual tracking and navigation prototype.     

A probabilistic model for breast cancer survival data

The aim of the present study is the development of a probabilistic model to analyze breast cancer survival data. A finite-state discrete-time Markov chain model has been formulated for the analysis of follow-up probability and mortality data for 780 breast cancer patients.

The proposed stochastic model can also be used in comparing the transition in order to estimate treatment effects.     

A probabilistic model for binaural sound localization.

This paper proposes a biologically inspired and technically implemented sound localization system to robustly estimate the position of a sound source in the frontal azimuthal half-plane. For localization, binaural cues are extracted using cochleagrams generated by a cochlear model that serve as input to the system. The basic idea of the model is to separately measure interaural time differences and interaural level differences for a number of frequencies and process these measurements as a whole. This leads to two-dimensional frequency versus time-delay representations of binaural cues, so-called activity maps. A probabilistic evaluation is presented to estimate the position of a sound source over time based on these activity maps. Learned reference maps for different azimuthal positions are integrated into the computation to gain time-dependent discrete conditional probabilities. At every timestep these probabilities are combined over frequencies and binaural cues to estimate the sound source position. In addition, they are propagated over time to improve position estimation. This leads to a system that is able to localize audible signals, for example human speech signals, even in reverberating environments.     

Traffic identification method based on multiple probabilistic neural network model

Neural Computing and Applications - Traffic identification is currently an important challenge for network management and security. In this paper, we propose a novel application identification...     

A probabilistic fuzzy logic system for modeling and control

In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.     

A probabilistic model for the fault tolerance of multilayerperceptrons

This paper presents a theoretical approach to determine the probability of misclassification of the multilayer perceptron (MLP) neural model, subject to weight errors. The type of applications considered are classification/recognition tasks involving binary input-output mappings. The analytical models are validated via simulation of a small illustrative example. The theoretical results, in agreement with simulation results, show that, for the example considered, Gaussian weight errors of standard deviation up to 22% of the weight value can be tolerated. The theoretical method developed here adds predictability to the fault tolerance capability of neural nets and shows that this capability is heavily dependent on the problem data.