Constructing fuzzy graphs from examples

Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. The underlying algorithm is demonstrated using several Java applets, which can be found under ‘Electronic annexes’ on www.elsevier.com/locate/ida.     

A new method for constructing membership functions and fuzzy rulesfrom training examples

To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the alpha-cuts of equivalence relations and the alpha-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.     

Learning templates from fuzzy examples in structural patternrecognition

Fuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that we can combine several possible definitions into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situations. However, in our previous approach, we need a human expert to define the templates for the fuzzy graph matching. This is usually tedious, time-consuming and error-prone. In this paper, we propose a learning algorithm that will, from a number of fuzzy examples, each of them being a FAG, find the smallest template that can be matched to the given patterns with respect to the matching metric.     

Generating fuzzy rules from training instances for fuzzy classification systems

In recent years, many methods have been proposed to generate fuzzy rules from training instances for handling the Iris data classification problem. In this paper, we present a new method to generate fuzzy rules from training instances for dealing with the Iris data classification problem based on the attribute threshold value α, the classification threshold value β and the level threshold value γ, where α  [0, 1], β  [0, 1] and γ  [0, 1]. The proposed method gets a higher average classification accuracy rate than the existing methods.     

Learning rules from incomplete training examples by rough sets

Machine learning can extract desired knowledge from existing training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete data sets. If some attribute values are unknown in a data set, it is called incomplete. Learning from incomplete data sets is usually more difficult than learning from complete data sets. In the past, the rough-set theory was widely used in dealing with data classification problems. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete data sets based on rough sets. A new learning algorithm is proposed, which can simultaneously derive rules from incomplete data sets and estimate the missing values in the learning process. Unknown values are first assumed to be any possible values and are gradually refined according to the incomplete lower and upper approximations derived from the given training examples. The examples and the approximations then interact on each other to derive certain and possible rules and to estimate appropriate unknown values. The rules derived can then serve as knowledge concerning the incomplete data set.     

基于模糊PID控制的微型燃气轮机发电系统研究

根据微型燃气轮发电机系统的动态特性,将微型燃气轮机及其电气部分当作一个整体,建立了微型燃气轮机发电系统完整的数学模型,并在转速环加入模糊PID控制器,分析了微型燃气轮机系统孤网带负荷时的动态特性。仿真结果验证了基于模糊PID控制的微型燃气轮机发电系统具有良好的稳定性和灵活性。     

Learning from examples: generation and evaluation of decision treesfor software resource analysis

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for one problem domain, specifically, that of software resource data analysis. The purpose of the decision trees is to identify classes of objects (software modules) that had high development effort, i.e. in the uppermost quartile relative to past data. Sixteen software systems ranging from 3000 to 112000 source lines have been selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4700 objects, capture a multitude of information about the objects: development effort, faults, changes, design style, and implementation style. A total of 9600 decision trees are automatically generated and evaluated. The analysis focuses on the characterization and evaluation of decision tree accuracy, complexity, and composition. The decision trees correctly identified 79.3% of the software modules that had high development effort or faults, on the average across all 9600 trees. The decision trees generated from the best parameter combinations correctly identified 88.4% of the modules on the average. Visualization of the results is emphasized, and sample decision trees are included     

Application of a system for the automatic generation of fuzzy neural networks

To facilitate the transfer of technology emerging from theoretical research of fuzzy neural networks into industrial applications, a fuzzy neural networks system for the automatic generation (FNNAGS) is proposed in this paper. In FNNAGS, the fuzzy model constructed by the system can be expressed as either a Mamdani model or a Takagi–Sugeno model, according to the preference of the user. Off-line design and on-line applications are incorporated into an interactive software system. In the stage of off-line design, only the training data need to be provided in order to construct a process model. Users do not need to give the initial fuzzy partitions, membership functions or fuzzy logic rules. These initial parameters will be set up automatically by the FNNAGS, in accordance with the properties of the training data. After off-line design has been completed, the model can be expressed as a fuzzy rule base, which can be used to control, estimate, identify or predict a process or plant through an application interface between FNNAGS and the external world.     

Parallelizing fuzzy rule generation using GPGPU

This article proposes a method to parallelize the process of generating fuzzy if-then rules for pattern classification problems in order to reduce the computational time. The proposed method makes use of general purpose computation on graphics processing units (GPGPUs)’ parallel implementation with compute unified device architecture (CUDA), a development environment. CUDA contains a library to perform matrix operations in parallel. In the proposed method, published source codes of matrix multiplication are modified so that the membership values of given training patterns with antecedent fuzzy sets are calculated. In a series of computational experiments, it is shown that the computational time is reduced for those problems that require high computational effort.     

Nonsmooth training of fuzzy neural networks

The integration of fuzzy methods and neural networks often leads to nonsmoothness of the neural network and, consequently, to a nonsmooth training problem. It is shown, that smooth training methods as e.g. backpropagation fail to converge in this case. Thus a method – based on so called bundle-methods – for training of nonsmooth neural network is presented. Numerical results obtained from a character recognition problem show, that this method still converges where backpropagation fails.     

Chinese adversarial examples generation approach with multi-strategy based on semantic

Knowledge and Information Systems - Recent studies have shown that after adding small perturbations that are imperceptible to humans, deep neural networks (DNNs) with good performance and popular...     

基于规范生成的文件模糊测试

为了解决传统文件模糊测试效率不高与功能遗漏的缺点,提出一种新的文件模糊测试算法.基于文件的规范,抽象地描述了文件推导规则,定义了文件模糊测试模板,设计了文件模糊变异模型.在规范描述下生成不同类型文件,然后对每类文件进行变异模糊测试,有效地减少了大量无效测试.实际测试中,已经验证3个已公开漏洞并发现两个未公开漏洞,表明了该算法的有效性.     

An improved SPSA algorithm for system identification using fuzzy rules for training neural networks

Simultaneous perturbation stochastic approximation (SPSA) belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation (FASPA) algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks (NNs). Accordingly, a multilayer perceptron (MLP) network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.     

Automatic fuzzy ontology generation for semantic Web

Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed.     

The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior

It is proposed to use evolutionary programming to generate finite state machines (FSMs) for controlling objects with complex behavior. The well-know approach in which the FSM performance is evaluated by simulation, which is typically time consuming, is replaced with comparison of the object’s behavior controlled by the FSM with the behavior of this object controlled by a human. A feature of the proposed approach is that it makes it possible to deal with objects that have not only discrete but also continuous parameters. The use of this approach is illustrated by designing an FSM controlling a model aircraft executing a loop-the-loop maneuver.     

Using multiple uncertain examples and adaptative fuzzy reasoning to optimize image characterization

This article proposes an automatic characterization method by comparing unknown images with examples more or less known. Our approach allows to use uncertain examples but easy to obtain (e.g. by automatic retrieval on the Internet). The use of fuzzy logic and adaptive clustering makes it possible to reduce automatically the noise from this database by preserving only the examples having a strong level of redundancy in the dominant shapes. To validate this method, we compared our artificial process of recognition with the estimation of human operators. The tests show that the automatic process gives an average accuracy of the characterization near to 95%.     

A fuzzy rule-based generation algorithm in interval type-2 fuzzy logic system for fault prediction in the early phase of software development

Reliability, a measure of software, deals in total number of faults count up to a certain period of time. The present study aims at estimating the total number of software faults during the early phase of software life cycle. Such estimation helps in producing more reliable software as there may be a scope to take necessary corrective actions for improving the reliability within optimum time and cost by the software developers. The proposed interval type-2 fuzzy logic-based model considers reliability-relevant software metric and earlier project data as model inputs. Type-2 fuzzy sets have been used to reduce uncertainties in the vague linguistic values of the software metrics. A rule formation algorithm has been developed to overcome inconsistency in the consequent parts of large number of rules. Twenty-six software project data help to validate the model, and a comparison has been provided to analyse the proposed model’s performance.     

Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system

This paper presents a systematic approach to design first order Tagaki-Sugeno-Kang (TSK) fuzzy systems. This approach attempts to obtain the fuzzy rules without any assumption about the structure of the data. The structure identification and parameter optimization steps in this approach are carried out automatically, and are capable of finding the optimal number of the rules with an acceptable accuracy. Starting with an initial structure, the system first tries to improve the structure and, then, as soon as an improved structure is found, it fine tunes its rules’ parameters. Then, it goes back to improve the structure again to find a better structure and re-fine tune the rules’ parameters. This loop continues until a satisfactory solution (TSK model) is found. The proposed approach has successfully been applied to well-known benchmark datasets and real-world problems. The obtained results are compared with those obtained with other methods from the literature. Experimental studies demonstrate that the predicted properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules. Finally, as a case study, the proposed approach is applied to the desulfurization process of a real steel industry. Comparing the proposed approach with some other fuzzy systems and neural networks, it is shown that the developed TSK fuzzy system exhibits better results with higher accuracy and smaller size of architecture.