车牌识别机器学习方法的数学建模与实现

首先,针对不同光照、复杂背景和投影失真的车牌图像建立基于Adaboost算法和改进Haar特征的车牌检测模型;然后,运用Radon变换进行车牌校正,并结合3次B样条小波变换和识别反馈模型对字符进行粗和精分割;最后,根据汉字和数字字母的不同结构特征,采用不同的算法提取特征,特别是针对车牌字符特点,训练汉字、字母和数字字母3种神经网络模型用于建立字符识别模型.实验结果表明该模型是实用的.     

Development of fuzzy state controller and its application to a hydraulic position servo

By combining control theory and fuzzy set theory, a new kind of state controller is proposed. Full order feedback and membership functions, which utilize the experience of experts, are used in the design of the state controller which we call a fuzzy state controller. Hydraulic position servos with a nonsymmetrical cylinder are commonly used in industry. This kind of system is nonlinear in nature and generally difficult to control. For different ending position, moving direction, strokes, and load the system dynamics is totally different. Once the above-mentioned parameters of the system are known, it is relatively straightforward to tune the gains of state controller to obtain good dynamic response. But when these parameters change, especially in case of the load, using the same gains will cause overshoot or even loss of system stability. Adaptive control is not applicable in this case due to the complexity of the algorithm, its rate of convergence, and the fast response characteristic of the system. The fuzzy state controller has been successfully applied to a hydraulic position servo. The system shows excellent robustness against variations of system parameters.     

Development of modular neural networks with fuzzy logic response integration for signature recognition

This paper describes a modular neural network (MNN) for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature, however this problem has proven to be a very difficult task. In this work, we propose an MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined by using a Sugeno fuzzy integral. The experimental results obtained by using a database of 30 individual’s shows that the modular architecture can achieve a very high 98% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system.     

Crucial combinations of parts for handwritten alphanumeric characters

A combining approach has been studied previously to integrate different parts of handwritten characters for their analysis and recognition. Perfect combinations, by which the characters can be identified with certainty, are important to pattern analysis and character recognition. However, a large number of possible combinations (e.g., 63 combinations for a character partitioned into six parts), also produce a lot of perfect combinations. Hence, it is necessary to determine which of them are most important. In this paper, we propose a methodology of finding the basic crucial combinations, and algorithms to compute them. Compared with perfect combinations, such basic crucial combinations are most significant to the character distinctiveness. Similarly, the largest confusion regions are also identified.Experimental studies have also been conducted using the 89 most frequently used patterns of 36 alphanumeric handprints, to obtain their basic crucial combinations and largest confusion combinations. The results indicate that the ratio of the number of basic crucial combinations to perfect combinations is only 12.6%, and the ratio of the number of the largest confusion regions to the total confusion combinations is 15.6%.     

Concepts of probabilistic sets

In the field of pattern recognition or decision making theory, the important subjects are as follows: (1) ambiguity of property of objects, (2) variety of character of objects, (3) subjectivity of observers, (4) evolution of knowledge of observers (i.e. learning). Considering these points, the concept of probabilistic set is proposed. It is based on both probability theory and fuzzy concepts. A probabilistic set on a total space is defined by a point wise measurable function from a parameter space (which is a probability space) to a characteristic space (which is a measurable space). It is shown that the family of all probabilistic sets constitutes a complete pseudo-Boolean algebra. Moment analysis is possible by using a probability measure of the parameter space. Other useful concepts are also mentioned such as probabilistic mappings and expected cardinal numbers.     

Data-driven fuzzy models for nonlinear identification of a complex heat exchanger

This paper presents and discusses experimental results on nonlinear model identification method applied to a real pilot thermal plant. The aim of this work is to develop a moderately complex model with interpretable structure for a complex parallel flow heat exchanger which is the main component of the thermal plant using a fuzzy clustering technique. The proposed Takagi–Sugeno-type (TS) fuzzy rule-based model is derived through an iterative fuzzy clustering algorithm using a set of input–output measurements. It is shown that the identified multivariable fuzzy rule-based model captures well the key dynamical properties of the physical plant over a wide operating range and under varying operating conditions. For validation, the model is run in parallel and series-parallel configurations to the real process. The experimental results show clearly the high performance of the proposed fuzzy model in achieving good prediction of the main process variables.     

Fuzzy sets-the approximation of semisets

This paper deals with alternative set theory which substantially departs from classical set theory. The main notion of it -a semiset-is proposed to model the same intuitive notion as fuzzy set but it is more general. The reasonable tool for ‘grasping’ semisets could be provided by fuzzy sets whose applicability is beyond discussion. In the paper reasons for such an approximation are given and two ways how to provide it are proposed. The fact that the membership function is modeled within the theory should be stressed. At the end, some problems and an indication for further investigation are discussed.     

基于模板匹配的车牌数字与字母识别

车牌的字符识别是车牌识别的重要组成部分.我国的车牌由7个字符组成,除第一位为汉字外,其余均为字母和数字字符,所以提高数字与字母识别率在车牌字符识别中占很重要的地位.通过模板匹配的方法对车牌的数字与字母字符进行识别.选择了两个判别函数,一个是求取模板、待识别字符与运算结果的标准差的最小值,另一个是求取运算结果与模板比值的最大值.并对两个判别函数的识别结果进行了比较,求标准差最小值在总体识别结果上比求比值最大值的识别结果要好,但在个别字符的识别上,求取比值最大值的识别结果要高于求取标准差最小值的识别结果.     

A novel approach to interval-valued intuitionistic fuzzy soft set based decision making

The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. The interval-valued intuitionistic fuzzy soft set is a combination of an interval-valued intuitionistic fuzzy set and a soft set. The aim of this paper is to investigate the decision making based on interval-valued intuitionistic fuzzy soft sets. By means of level soft sets, we develop an adjustable approach to interval-valued intuitionistic fuzzy soft sets based decision making and some numerical examples are provided to illustrate the developed approach. Furthermore, we also define the concept of the weighted interval-valued intuitionistic fuzzy soft set and apply it to decision making.     

Indirect adaptive fuzzy observer and controller design based on interval type-2 T-S fuzzy model

This paper presents the design scheme of the indirect adaptive fuzzy observer and controller based on the interval type-2 (IT2) T-S fuzzy model. The nonlinear systems can be well approximated by IT2 T-S fuzzy model, in which the fuzzy rules’ antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The proposed IT2 T-S fuzzy model is a combination of IT2 fuzzy system and T-S fuzzy model, and also inherits the benefits of type-2 fuzzy logic systems, which is able to directly handle uncertainties and can minimize the effects of uncertainties in rule-based fuzzy system. These characteristics can improve the accuracy of the system modeling and reduce the number of system rules. The proposed method using feedback control, adaptive laws, and on-line object parameters are adjusted to ensure observation error bounded. In addition, using Lyapunov synthesis approach and Lipschitz condition, the stability analysis is conducted. The simulation results show that the proposed method can handle unpredicted disturbance and data uncertainties very well in advantage of the effectiveness of observation and control.     

On Optimal Forecasting with Soft Computation for Nonlinear Time Series

In this paper we present a new approach on optimal forecasting by using the fuzzy set theory and soft computing methods for the dynamic data analysis. This research is based on the concepts of fuzzy membership function as well as the natural selection of evolution theory. Some discussions in the sensitivity of the design of fuzzy processing will be provided. Through the design of genetic evolution, the AIC criteria is used as the adjust function, and the fuzzy memberships function of each gene model are calculated. Simulation and empirical examples show that our proposed forecasting technique can give an optimal forecasting in time series analysis.     

Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the interpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretability-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns.     

模式识别的Fuzzy统计方法及应用

运用模糊数学的概念和方法对具有模糊性的观测结果进行处理和识别 ,构成了模糊模式识别的基本内容 .利用 Fuzzy统计的方法建立了模式识别的数学模型 ,并编出模型的计算机程序 .按照科技部、教育部关于科研评价的“目标导向、分类实施、客观公正、注重实效”的要求 ,将上述程序应用于科研项目的评审中 ,克服了以往评审过程单一化、主观化的缺点 ,使评审客观、公正、合理 ,易于操作 .     

Some relationships between fuzzy and random set-based classifiers and models

When designing rule-based models and classifiers, some precision is sacrificed to obtain linguistic interpretability. Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models. Unless performance of both approaches is equivalent, it is difficult to judge whether the fuzzy one is doing its best, because the precision gap between the best understandable model and the best black-box model is not known.In this paper we discuss how to generate probabilistic rule-based models and classifiers with the same structure as fuzzy rule-based ones. Fuzzy models, in which features are partitioned into linguistic terms, will be compared to probabilistic rule-based models with the same number of terms in every linguistic partition. We propose to use these probabilistic models to estimate a lower precision limit which fuzzy rule learning algorithms should surpass.     

模糊环境下的一种多准则决策方法

本文利用模糊集理论以及层次分析法（AHP）原理，建立了一种在模糊环境下对方案进行择优或排序的多准则决策方法。     

基于双论域模糊粗糙集的应急决策模型与方法

以突发危机事件应急决策为应用背景,讨论了双论域上模糊粗糙集的基本理论,建立了基于模糊相容关系的双论域模糊粗糙集模型. 在此基础上,把突发危机事件应急决策转化为一个具有模糊决策对象的双论域决策近似空间上的粗糙近似问题,构建了基于双论域模糊粗糙集的应急决策模型.首先在双论域近似空间中计算模糊决策对象的上(下)近似,进而结合经典非确定型决策的思想给出了突发危机事件应急决策的规则.同时,给出了模型的算法.该模型给出了一种在不完全信息环境下应急决策的方法,给出了在充分考虑决策者个人偏好信息基础上的决策置信度以及最优决策规则.该方法能够比较充分地符合应急决策信息不充分、资源有限以及时间紧迫的基本特征, 进而对突发危机事件应急决策提供科学的理论基础和现实的决策方法.最后,通过应用算例说明了模型的应用过程,结果验证了本文给出模型的有效性。     

模糊综合评判在高校排课系统评价中的应用

模糊综合评判是一种应用广泛的模糊数学方法,是在综合考虑和整体平衡中顾及各种因素全面进行评判的科学方法,可广泛应用在各类人员、工作、产品等等客体的综合评判工作中.本文主要采用模糊数学理论对我院排课系统进行综合评价,经确定评判因素集、权重、等级评语向量和评价矩阵等,从而得到了三级模糊综合评判模型.结果表明该模型用于排课系统评估,客观公正,可信度高.     

Interpretability assessment of fuzzy knowledge bases: A cointension based approach

Computing with words (CWW) relies on linguistic representation of knowledge that is processed by operating at the semantical level defined through fuzzy sets. Linguistic representation of knowledge is a major issue when fuzzy rule based models are acquired from data by some form of empirical learning. Indeed, these models are often requested to exhibit interpretability, which is normally evaluated in terms of structural features, such as rule complexity, properties on fuzzy sets and partitions. In this paper we propose a different approach for evaluating interpretability that is based on the notion of cointension. The interpretability of a fuzzy rule-based model is measured in terms of cointension degree between the explicit semantics, defined by the formal parameter settings of the model, and the implicit semantics conveyed to the reader by the linguistic representation of knowledge. Implicit semantics calls for a representation of user’s knowledge which is difficult to externalise. Nevertheless, we identify a set of properties - which we call “logical view” - that is expected to hold in the implicit semantics and is used in our approach to evaluate the cointension between explicit and implicit semantics. In practice, a new fuzzy rule base is obtained by minimising the fuzzy rule base through logical properties. Semantic comparison is made by evaluating the performances of the two rule bases, which are supposed to be similar when the two semantics are almost equivalent. If this is the case, we deduce that the logical view is applicable to the model, which can be tagged as interpretable from the cointension viewpoint. These ideas are then used to define a strategy for assessing interpretability of fuzzy rule-based classifiers (FRBCs). The strategy has been evaluated on a set of pre-existent FRBCs, acquired by different learning processes from a well-known benchmark dataset. Our analysis highlighted that some of them are not cointensive with user’s knowledge, hence their linguistic representation is not appropriate, even though they can be tagged as interpretable from a structural point of view.     

Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems

Attribute reduction is viewed as an important issue in data mining and knowledge representation. This paper studies attribute reduction in fuzzy decision systems based on generalized fuzzy evidence theory. The definitions of several kinds of attribute reducts are introduced. The relationships among these reducts are then investigated. In a fuzzy decision system, it is proved that the concepts of fuzzy positive region reduct, lower approximation reduct and generalized fuzzy belief reduct are all equivalent, the concepts of fuzzy upper approximation reduct and generalized fuzzy plausibility reduct are equivalent, and a generalized fuzzy plausibility consistent set must be a generalized fuzzy belief consistent set. In a consistent fuzzy decision system, an attribute set is a generalized fuzzy belief reduct if and only if it is a generalized fuzzy plausibility reduct. But in an inconsistent fuzzy decision system, a generalized fuzzy belief reduct is not a generalized fuzzy plausibility reduct in general.