计算机语音模糊模式识别

本文介绍了计算机语音模糊模式识别的三个主要组成部分:特征分析与抽取的硬件系統;特征处理与应用管理的软件系统;模糊线性匹配动态规划匹配以及特征空间β截集分析法的模糊数学分析处理方法。本文所涉及的计算机汉语语音模式识别系统已经在微机上实现,并在军事指挥系统等方面得到应用。     

小卫星在轨故障诊断的模糊神经网络模型

小卫星高性能，高自主的发展趋势对于在轨故障诊断技术的实现要求日益迫切，而受小卫星体积小，重量轻，能源少的限制，当前常用的建立在高性能计算机硬件基础上的各种诊断方法不再适用于强调实时性，准确性的在轨运行监测，诊断与恢复和重构重处理。本文小卫星一体化系统总体设计技术研究与集成化设计系统为基础，采用一种神经网络与模糊系统相结合的模糊神经网络（ＦＮＮ）模型来分区域表示诊断系统并基于该ＦＮＮ模型进行诊断推量     

一种基于模糊数中心的模糊数排序方法

模糊数的排序法在决策及其它模糊应用系统的研究中起着非常重要的作用，众多学者提出了很多模糊数的排序方法，Cheng和Chu提出两种与模糊数中心有关的排序指标。但这两种方法都有明显的缺陷。本文构造了新的排序指标，能有效地实现各种模糊数的排序，最后用实例与前两种排序指标进行比较，体现出新指标的优越性。     

模糊数学在飞机故障诊断中的应用

飞机故障征兆与故障原因问的隶属关系是模糊的，为此，将模糊数学方法引入到飞机故障诊断中，提出由历史数据及专家优序数综合确定模糊隶属度的方法，建立模糊诊断模型，并在计算机上实现飞机故障的模糊诊断。     

多层次指标投影与模糊综合评判结论的因果分析

数据包络分析方法与模糊综合评判方法是两个十分重要但又相互独立的评价方法，如果能够找到两种方法的关联关系，进而实现两种方法的优势互补将是一项值得探讨的工作。另外，模糊指标合成后，要想应用数据包络分析方法找到更微观指标的改进信息也非常困难。为解决这些问题，本文首先基于模糊综合评判的已有信息，构建多层次模糊评价结果的可能集，并提出多层次模糊投影的定义及相应的计算公式。在此基础上，给出了测度模糊事件存在不足的定量方法。最后，以中国14个旅游省区的游客满意度为例进行了实证分析。     

不确定对象的自学习模糊神经网络控制方法

本文对具有不确定性控制对象提出了一种自学习模糊神经网络控制方法，模糊控制器采用误差，误差变化及误差加速度的加权和解析描述形式，利用人工神经网络直接对过程的建模，实现对模糊加权因子的自学习优化调整。将上述方法用于焊接熔池动态过程控制实实验，结果表明本文提出的自学习模糊神经网络控制方案有效。     

基于模糊穴的轨道交通安全评估方法的研究及应用

提出一种基于模糊穴的轨道交通安全评估方法FCRS（Fuzzy-Cell based Rail traffic Safety analysis）。该方法从轨道交通安全相关要素的辨识，内外部属性的动态演化过程，分析轨道交通安全状态的动态演变机理。利用神经网络实现了模糊穴模型。实验及应用表明该方法在改善主动安全预警水平中取得了较好的效果。     

城市主干道交通信号灯模糊线控制的探讨

本文首先提出城市交通系统线控制的两级递阶结构：第一级，用模糊逻辑控制器确定单路口交通信号灯的周期和绿信比；第二级，用模糊相位控制器确定相邻两路口的相位差；两级间用模糊转换开关协调；然后，提出具体的实现方法。     

一种算子模糊逻辑系统及其Petri网推理算法

定义一种只带模糊文字以及模糊算子的模糊逻辑，讨论了该逻辑的λ-归结的相容性、完备性及其若干逻辑性质。为了实现算子模糊逻辑的归结推理，给出了算子模糊逻辑的Petri网模型：Horn型，进一步讨论了推理算法：T-不变量算法，得到了算法的完备性定理，最后用实例进行了验证。     

单源模糊数及其运算

本文首先指出模糊数的运算存在模糊源问题，然后定义了单源模糊数的一些基本概念，并建立了单源模糊数的运算方法，最后给出了单源模糊数线性方程组的求解方法。     

可加性模糊系统(AFS)权系数W的最小二乘估计

本文对模糊规则的前件进行非模糊化,从而给出了被广泛应用于现代控制理论中的可加性模糊系统（ＡＦＳ）权系数Ｗ 的最小二乘估计。     

Coordinated control of distribution supply chains in the presence of fuzzy customer demand

This paper considers a single product inventory control in a Distribution Supply Chain (DSC). The DSC operates in the presence of uncertainty in customer demands. The demands are described by imprecise linguistic expressions that are modelled by discrete fuzzy sets. Inventories at each facility within the DSC are replenished by applying periodic review policies with optimal order up-to-quantities. Fuzzy customer demands imply fuzziness in inventory positions at the end of review intervals and in incurred relevant costs per unit time interval. The determination of the minimum of defuzzified total cost of the DSC is a complex problem which is solved by applying decomposition; the original problem is decomposed into a number of simpler independent optimisation subproblems, where each retailer and the warehouse determine their optimum periodic reviews and order up-to-quantities. An iterative coordination mechanism is proposed for changing the review periods and order up-to-quantities for each retailer and the warehouse in such a way that all parties within the DSC are satisfied with respect to total incurred costs per unit time interval. Coordination is performed by introducing fuzzy constraints on review periods and fuzzy tolerances on retailers and warehouse costs in local optimisation subproblems.     

A two warehouse inventory model for a deteriorating item with partially/fully backlogged shortage and fuzzy lead time

An optimization inventory policy for a deteriorating item with imprecise lead-time, partially/fully backlogged shortages and price dependent demand is developed under two-warehouse system. For display and storage, the retailer hires one warehouse of finite capacity at market place, treated as own warehouse (OW) and another warehouse of large capacity as it may be required at a distance place from the market, treated as rented warehouse (RW). Holding cost at RW decreases with the increase of distance from the market place. Units are transferred from RW to OW in bulk release pattern and sold from OW. Using the nearest interval approximation method the estimated fuzzy average profit function is defuzzified and transformed to multiple crisp objective functions which are solved by Global Criteria Method. The models are illustrated numerically. Sensitivity of the inventory costs on the location of RW has been depicted graphically. Also loss in profit due to deteriorations for both models have been presented.     

An algorithm for a fuzzy transportation problem to select a new type of coal for a steel manufacturing unit

Manufacturing of steel involves thermal energy intensive processes with coal as the major input. Energy generated is a direct function of ash content of coal and as such it weighs very high as regards the choice of coal. In this paper, we study a multiobjective transportation problem to introduce a new type of coal in a steel manufacturing unit in India. The use of new type of coal serves three non-prioritized objectives, viz. minimization of the total freight cost, the transportation time and the ratio of ash content to the production of hot metal. It has been observed from the past data that the supply and demand points have shown fluctuations around their estimated values because of changing economic conditions. To deal with uncertainties of supply and demand parameters, we transform the past data pertaining to the amount of supply of the ith supply point and the amount of demand of the jth demand point using level (λ,ρ) interval-valued fuzzy numbers. We use a linear ranking function to defuzzify the fuzzy transportation problem. A transportation algorithm is developed to find the non-dominated solutions for the defuzzified problem. The application of the algorithm is illustrated by numerical examples constructed from the data provided by the manufacturing unit.      

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.     

Hardware Implementation of Fuzzy PID Controllers

For traditional hardware implementation of fuzzy PID controllers, it is large at computation and bad in real-time performance, so, a kind of PID control algorithm, whose gain parameters could be tuned by their fuzzy system, was selected as studying example for a novel idea of hardware implementation. In this paper, authors presented hardware network of memory address mapping to implement fuzzy PID control algorithm, and designed the corresponding hardware system. The idea actually realizes fusion of hardware and intelligent algorithm. The implementation effectively simplified hardware circuits, the whole controller is very simple without CPU. Meanwhile, it is very easy to use, only connecting the sensor/transducer, the driver and the actuator is OK. The controller is very rapid in response, it need only two A/D conversion periods for outputting a required control signal. So the implementation could meet real-time performance effectively.     

Possibility and necessity constraints and their defuzzification—A multi-item production-inventory scenario via optimal control theory

In this paper, analogous to chance constraints, real-life necessity and possibility constraints in the context of a multi-item dynamic production-inventory control system are defined and defuzzified following fuzzy relations. Hence, a realistic multi-item production-inventory model with shortages and fuzzy constraints has been formulated and solved for optimal production with the objective of having minimum cost. Here, the rate of production is assumed to be a function of time and considered as a control variable. Also the present system produces some defective units along with the perfect ones and the rate of produced defective units is constant. Here demand of the good units is time dependent and known and the defective units are of no use. The space required per unit item, available storage space and investment capital are assumed to be imprecise. The space and budget constraints are of necessity and/or possibility types. The model is formulated as an optimal control problem and solved for optimum production function using Pontryagin’s optimal control policy, the Kuhn–Tucker conditions and generalized reduced gradient (GRG) technique. The model is illustrated numerically and values of demand, optimal production function and stock level are presented in both tabular and graphical forms. The sensitivity of the cost functional due to the changes in confidence level of imprecise constraints is also presented.     

Biplots of fuzzy coded data

A biplot, which is the multivariate generalization of the two-variable scatterplot, can be used to visualize the results of many multivariate techniques, especially those that are based on the singular value decomposition. We consider data sets consisting of continuous-scale measurements, their fuzzy coding and the biplots that visualize them, using a fuzzy version of multiple correspondence analysis. Of special interest is the way quality of fit of the biplot is measured, since it is well known that regular (i.e., crisp) multiple correspondence analysis seriously under-estimates this measure. We show how the results of fuzzy multiple correspondence analysis can be defuzzified to obtain estimated values of the original data, and prove that this implies an orthogonal decomposition of variance. This permits a measure-of-fit to be calculated in the familiar form of a percentage of explained variance, which is directly comparable to the corresponding fit measure used in principal component analysis of the original data. The approach is motivated initially by its application to a simulated data set, showing how the fuzzy approach can lead to diagnosing nonlinear relationships, and finally it is applied to a real set of meteorological data.     

Analysis of a fuzzy economic order quantity model for deteriorating items under retailer partial trade credit financing in a supply chain

This paper investigates the economic order quantity (EOQ) — based inventory model for a retailer under two levels of trade credit to reflect the supply chain management situation in the fuzzy sense. It is assumed that the retailer maintains a powerful position and can obtain the full trade credit offered by the supplier yet the retailer just offers a partial trade credit to customers. The demand rate, holding cost, ordering cost, purchasing cost and selling price are taken as fuzzy numbers. Under these conditions, the retailer can obtain the most benefits. Study also investigates the retailer’s inventory policy for deteriorating items in a supply chain management situation as a cost minimization problem in the fuzzy sense. The annual total variable cost for the retailer in fuzzy sense is defuzzified using Graded Mean Integration Representation method. Then the present study shows that the defuzzified annual total variable cost for the retailer is convex, that is, a unique solution exists. Mathematical theorems and algorithms are developed to efficiently determine the optimal inventory policy for the retailer. Numerical examples are given to illustrate the theorems and the algorithms. Finally, the results in this paper generalize some already published results in the crisp sense.     

A fuzzy voting scheme for hardware and software fault tolerant systems

Voting algorithms are used to arbitrate between the results of redundant modules in fault-tolerant systems. Inexact majority and weighted average voters have been used in many applications, although both have problems associated with them. Inexact majority voters require an application-specific 'voter threshold’ value to be specified, whereas weighted average voters are unable to produce a benign output when no agreement exists between the voter inputs. Neither voter type is able to cope with uncertainties associated with the voter inputs. This paper introduces a novel voting scheme based on fuzzy set theory. It softens the harsh behaviour of the inexact majority voter in the neighbourhood of the ‘voter threshold’, and handles uncertainty and some multiple error cases in the region defined by the fuzzy input variables. The voter assigns a fuzzy difference value to each pair of voter inputs based on their numerical distance. A set of fuzzy rules then determines a single fuzzy agreeability value for each individual input which describes how well it matches the other inputs. The agreeability of each voter input is then defuzzified to give a weighting value for that input which determines its contribution to the voter output. The weight values are then used in the weighted average algorithm for calculating the voter final output. The voter is experimentally evaluated from the point of view safety and availability, and compared with the inexact majority voter in a Triple Modular Redundant structured framework. The impact of changing some fuzzy variables on the performance of the voter is also investigated. We show that the fuzzy voter gives more correct outputs (higher availability) than the inexact majority voter with small and large errors, less incorrect outputs (higher safety) than the inexact majority voter in the presence of small errors, and less benign outputs than the inexact majority voter. The percentage of the benign outputs of the majority voter that are successfully handled by the fuzzy voter (resulting in correct outputs) is more than the percentage of those that are unsuccessfully resolved by the fuzzy voter (resulting in incorrect outputs). Our results suggest that the fuzzy voter is a viable alternative to a traditional inexact voter in cases where the benefits of a large increase in availability, and a considerable decrease in the number of benign outputs outweighs the cost of a small degradation in the safety performance of the system. The fuzzy voter is also a useful voting algorithm when arbitrating between the responses of dynamic channels of control systems incorporating uncertainties. This is the first reported use of a complete fuzzy voter in the context of fault tolerance.