逻辑系统$G_3$中命题的真度值之集在[0,1]上的分布

利用势为3的均匀概率空间的无穷乘积在G■del三值命题逻辑中引入了公式的真度概念,给出了真度推理规则,证明了全体公式的真度值之集在[0,1]上是稠密的,并给出了公式真度的表达通式,为进一步建立三值命题逻辑的近似推理理论奠定了基础．     

A pattern-directed approach to FMS scheduling

Scheduling in flexible manufacturing systems (FMS) must take into account the shorter lead time, the multiprocessing environment, and the dynamically changing states. In this paper, a pattern-directed approach is presented which incorporates a nonlinear planning method developed in the artificial intelligence field. The scheduling system described here is knowledge-based and utilizes both forward-and backward-chaining for generating schedules (treated as state-space plans). The pattern-directed approach is dynamically adjustable and thus can handle scheduling requirements unique to the FMS environment, such as dynamic scheduling, failure-recovery scheduling, or prioritized scheduling for meeting deadlines.     

Inductive inference of ultimately periodic sequences

An algorithm is presented for inductive inference of ultimately periodic sequences. It can be implemented in linear time and space.Dedicated to Peter Naur on the occasion of his 60th birthday     

Reject inference,augmentation, and sample selection

Many researchers see the need for reject inference in credit scoring models to come from a sample selection problem whereby a missing variable results in omitted variable bias. Alternatively, practitioners often see the problem as one of missing data where the relationship in the new model is biased because the behaviour of the omitted cases differs from that of those who make up the sample for a new model. To attempt to correct for this, differential weights are applied to the new cases. The aim of this paper is to see if the use of both a Heckman style sample selection model and the use of sampling weights, together, will improve predictive performance compared with either technique used alone. This paper will use a sample of applicants in which virtually every applicant was accepted. This allows us to compare the actual performance of each model with the performance of models which are based only on accepted cases.     

Expert systems based on LCTV AND/OR logic

A problem in expert systems is typically represented by an AND/OR graph. A set of spatial light modulators which can perform AND and OR logic operations can directly be applied to form an optical inference engine. The threshold-dependence logic operations of relatively low contrast and small dynamic range liquid crystal television are explained. The parallel inference architecture operated by liquid crystal televisions is presented.     

A site-specific and multi-element isotopic approach to origin inference of sugars in foods and beverages

A strategy is presented for the characterization of sugars according to their botanical origin. The samples fermented in standardized conditions can be described in the multi-dimensional space of the overall carbon isotope ratio of ethanol measured by isotope ratio mass spectrometry (IRMS) and of the specific hydrogen isotope parameters of the methyl and methylene sites derived from nuclear magnetic resonance investigation of site-specific natural isotope fractionation (SNIF-NMR method). In the comparison of natural juices, the deuterium and oxygen-18 parameters of water extracted from the juice and from the end fermentation medium also contain information on the origin of the product. The isotopic effects of the concentration processes leading to concentrated juices, musts and syrups can be estimated and taken into account in interpreting the data.The classification power of this multi-element and multi-site approach is illustrated by discriminant analyses involving selected isotopic variables associated with pineapple, apple and barley sugars, compared to beet and cane sugars which are common sources of enrichment. The ability of the method to detect adulteration by exogeneous sugars is improved when environmental conditions can be taken into account.     

Design analysis for optimal calibration of diffusivity in reactive multilayers

Calibration of the uncertain Arrhenius diffusion parameters for quantifying mixing rates in Zr–Al nanolaminate foils have been previously performed in a Bayesian setting [M. Vohra, J. Winokur, K.R. Overdeep, P. Marcello, T.P. Weihs, and O.M. Knio, Development of a reduced model of formation reactions in Zr–Al nanolaminates, J. Appl. Phys. 116(23) (2014): Article No. 233501]. The parameters were inferred in a low-temperature, homogeneous ignition regime, and a high-temperature self-propagating reaction regime. In this work, we extend the analysis to determine optimal experimental designs that would provide the best data for inference. We employ a rigorous framework that quantifies the expected information gain in an experiment, and find the optimal design conditions using Monte Carlo techniques, sparse quadrature, and polynomial chaos surrogates. For the low-temperature regime, we find the optimal foil heating rate and pulse duration, and confirm through simulation that the optimal design indeed leads to sharp posterior distributions of the diffusion parameters. For the high-temperature regime, we demonstrate the potential for increasing the expected information gain concerning the posteriors by increasing the sample size and reducing the uncertainty in measurements. Moreover, posterior marginals are also obtained to verify favourable experimental scenarios.     

A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management

This paper presents a hybrid neuro-fuzzy methodology to identify appropriate global logistics (GL) operational modes used for global supply chain management. The proposed methodological framework includes three main developmental phases: (1) establishment of a GL strategic hierarchy, (2) formulation of GL-mode identification rules, and (3) development of a GL-mode choice model. By integrating advanced multi-criteria decision-making (MCDM) techniques including fuzzy analytical hierarchy process (Fuzzy-AHP), Fuzzy-MCDM, and the technique for order preference by similarity to an ideal solution (TOPSIS), six types of global logistics and operational modes coupled with corresponding fuzzy-based multi-criteria decision-making rules are specified in the second phase. Using the specified fuzzy decision-making rules as the input database, an adaptive neuro-fuzzy inference system (ANFIS) is then developed in the third phase to identify proper GL modes for the implementation of global supply chain management. A numerical study with a questionnaire survey database aimed at the information technology (IT) industries of Taiwan is conducted to illustrate the applicability of the proposed method.     

线性回归模型的Bayes统计推断

Bayes方法虽融合了样本信息和先验信息，但利用的先验信息都是有历史经验和专家估计所得，因此可靠度不高。该文研究了正态线性回归模型：Y=Xβ＋e，e—N（0，σ^2。L），其中σ^2已知，β为未知参数向量，对传统的Bayes方法进行了改进，即把Bayes方法中的后验信息作为改进Bayes的无验信息并融合样本信息进行统计推断，在二次损失函数下得到了β的改进的Bayes估计。由于改进的Bayes方法的先验信息中有样本信息，因此其准确度比传统的Bayes方法准确度更高。     

Parameter inference in small world network disease models with approximate Bayesian Computational methods

Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed “guesses” of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.