On multistage fuzzy neural network modeling

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fuzzy neural network (FNN) models have been proposed to implement different types of single-stage fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problems. To address the problem, FNN modeling based on multistage fuzzy reasoning (MSFR) is pursued here and two hierarchical network models, namely incremental type and aggregated type, are introduced. The new models called multistage FNN (MSFNN) model a hierarchical fuzzy rule set that allows the consequence of a rule passed to another as a fact through the intermediate variables. From the stipulated input-output data pairs, they can generate an appropriate fuzzy rule set through structure and parameter learning procedures proposed in this paper. In addition, we have particularly addressed the input selection problem of these two types of multistage network models and proposed two efficient methods for them. The effectiveness of the proposed MSFNN models in handling high-dimensional problems is demonstrated through various numerical simulations     

基于BP网络的模糊Petri网的学习能力

模糊Petri网(Fuzzy Petri Nets，FPN)是基于模糊产生式规则的知识库系统的良好建模工具，但自学习能力差是模糊系统本身的一个缺点．该文提出了适合模糊Petri网模型自学习的模糊推理算法和学习算法．在模糊推理算法中，通过对没有回路的FPN模型结构进行层次式划分以及建立变迁点燃和模糊推理的近似连续函数，从而把神经网络中的BP网络算法自然地引入到FPN模型中．在FPN模型上，用误差反传算法计算一阶梯度的方法对模糊产生式规则中的参数进行学习和训练．经过学习和训练的FPN具有很强的泛化能力和自适应功能．FPN模型经过训练得到的参数是有特定含义的，可以通过对这些参数的合法性分析，使得模糊产生式规则系统更加有效，也对知识库系统的建立、更新和维护有着重要的意义．     

Adaptive fuzzy spiking neural P systems for fuzzy inference and learning

Fuzzy spiking neural P systems (in short, FSN P systems) are a novel class of distributed parallel computing models, which can model fuzzy production rules and apply their dynamic firing mechanism to achieve fuzzy reasoning. However, these systems lack adaptive/learning ability. Addressing this problem, a class of FSN P systems are proposed by introducing some new features, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowledge base but also can perform dynamically fuzzy reasoning. It is important to note that AFSN P systems have learning ability like neural networks. Based on neuron's firing mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. Moreover, an example is included to illustrate the learning ability of AFSN P systems.     

Cascaded fuzzy neural network model based on syllogistic fuzzyreasoning

In recent years, there has been an increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fuzzy neural network (FNN) models have been proposed to implement different types of single-stage fuzzy reasoning mechanisms. Single-stage fuzzy reasoning, however, is only the most basic among a human being's various types of reasoning mechanisms. Syllogistic fuzzy reasoning, where the consequence of a rule in one reasoning stage is passed to the next stage as a fact, is essential to effectively build up a large scale system with high level intelligence. In view of the fact that the fusion of syllogistic fuzzy logic and neural networks has not been sufficiently studied, a new FNN model based on syllogistic fuzzy reasoning, termed cascaded FNN (CFNN), is proposed in this paper. From the stipulated input-output data pairs, the model can generate an appropriate syllogistic fuzzy rule set through structure (genetic) learning and parameter (back-propagation) learning procedures proposed in this paper. In addition, we particularly discuss and analyze the performance of the proposed model in terms of approximation ability and robustness as compared with single-stage FNN models. The effectiveness of the proposed CFNN model is demonstrated through simulating two benchmark problems in fuzzy control and nonlinear function approximation domain     

A Fuzzy Inference Network Model for Search Strategy Using Neural Logic Network

In this paper, a fuzzy inference network model for search strategy using neural logic network is presented. The model describes search strategy, and neural logic network is used to search. Fuzzy logic can bring about appropriate inference results by ignoring some information in the reasoning process. Neural logic networks are powerful tools for the reasoning process but not appropriate for the logical reasoning. To model human knowledge, besides the reasoning process capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct a fuzzy inference network model based on the neural logic network, extending the existing rule inference network. And the traditional propagation rule is modified.     

关系结构中的神经计算

人工神经网络在许多应用领域中建立了一种新的高度并行计算的结构。具有表示概念和知识的模糊集合被认为是处理人们日常生活中不确定问题的一种工具。特别是关系结构,它在构造现实世界中的形式关系模型中起着非常重要的作用。本文根据模糊集合理论,提出新的神经网络结构及其新的学习算法,并研究其性质。一方面把模糊集合的方法学应用于神经网络结构和学习算法的研究,同时使得神经网络的硬件实现更加容易。     

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi–Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data-driven online learning approach includes not only the adaptation of linear parameters appearing in the rule consequents, but also the incremental learning of premise parameters appearing in the membership functions (fuzzy sets), together with a rule learning strategy in sample mode. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include an automatic generation of new clusters based on the nature, distribution, and quality of new data and an alternative strategy for selecting the winning cluster (rule) in each incremental learning step. Antecedent and consequent learning are connected in a stable manner, meaning that a convergence toward the optimal parameter set in the least-squares sense can be achieved. An evaluation and a comparison to conventional batch methods based on static and dynamic process models are presented for high-dimensional data recorded at engine test benches and at rolling mills. For the latter, the obtained data-driven fuzzy models are even compared with an analytical physical model. Furthermore, a comparison with other evolving fuzzy systems approaches is carried out based on nonlinear dynamic system identification tasks and a three-input nonlinear function approximation example.      

基于NNFPN 模型的电梯故障诊断方法的研究

提出一种基于神经网络的模糊Petri网模型(NNFPN)，并将该模型应用于电梯故障诊断系统．阐述了该模型的定义、模糊产生式规则的NNFPN表示、NNFPN的触发规则以及NNFPN的训练学习算法．分析了如何建立基于NNFPN的电梯故障诊断模型，提出针对不同工作状态利用不同的NNFPN电梯故障模型进行诊断，可使诊断结果更加准确．     

An approach to fuzzy default reasoning for function approximation

This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap with each other in the input space. General rules work as default rules in our fuzzy rule base. First, we briefly describe existing approaches to the handling of default rules in the framework of possibility theory. Next, we show that standard interpolation-based fuzzy reasoning leads to counterintuitive results when general rules include specific rules with different consequents. Then, we demonstrate that intuitively acceptable results are obtained from a non-standard inclusion-based fuzzy reasoning method. Our approach is based on the preference for more specific rules, which is a commonly used idea in the field of default reasoning. When a general rule includes a specific rule and they are both compatible with an input vector, the weight of the general rule is discounted in fuzzy reasoning. We also discuss the case where general rules do not perfectly but partially include specific rules. Then we propose a genetics-based machine learning (GBML) algorithm for extracting a small number of fuzzy if-then rules with different specificity levels from numerical data using our inclusion-based fuzzy reasoning method. Finally, we describe how our approach can be applied to the approximate realization of fuzzy number-valued nonlinear functions     

基于Elman神经网络的浓相输送模糊控制系统

由于粉末物料的浓相输送系统存在严重的非线性和时变性,故要想建立其准确数学模型难度非常大,本文提出了使用模糊神经网络控制系统,并对于模糊控制规则由Elman神经网络联想记忆后提取,它不但可以获得最佳控制规则,而且响应速度快并能够进行在线进行规则的修正。经仿真实验,该控制器能够对粉末物料流量在一定范围内进行协调优化时实控制。     

Analytical resolution and numerical identification of fuzzyrelational systems

Since Sanchez's seminal paper on fuzzy relational equations, both their theories and applications have been continuously exploited by researchers. However, the solvable conditions of a system of fuzzy relational equations, also known as a fuzzy relational system (FRS), are still poorly established and their relationship with the methods for obtaining approximate solutions are unclear. When the FRS is adopted to model a fuzzy system, most of the existing identification algorithms focus on parameter estimation and less on the structure identification. In this paper, these two issues are addressed. New theoretical understandings on solving a system of fuzzy relational equations exactly and approximately are presented and their implications on the use of FRS to encode fuzzy rulebases are highlighted. Based upon the guided evolutionary simulated annealing (GESA) algorithm, an evolutionary identification formulation called EVIDENT capable for both parameter and structure identifications in fuzzy system models is proposed. As demonstrated by the simulation results, the new algorithm not only is effective in determining the structure of the systems, but also identifies a better parametric solution, as compared with that of the existing FRS identification algorithms.     

基于双枝模糊逻辑和模糊着色Petri网的攻击模型

定义了一种基于双枝模糊逻辑和模糊着色Petri网的网络攻击模型, 从对攻击起促进和抑制作用这两方面对网络攻击进行综合考虑与分析, 同时对模糊规则库中的不同变量用不同的颜色来区分, 因此可构成一个简明的BBFCPN模型。在此基础上, 给出了BBFCPN模型的基本推理规则和推理算法。针对攻击实例的分析进一步验证了提出的模型及相关推理算法。     

自适应模糊神经网络研究

模糊神经网络提供了从人工神经网络中模糊规则的抽取。本文研究模糊神经网络的自适应学习，规则插入和抽取及神经－模糊推理的FuNN模型，把遗传算法作为系统模糊规则选择的自适应策略之一。     

基于模糊推理的教学策略推理机的算法设计

详细阐述了模糊推理系统与实现模糊推理机工作流程设计的方法和算法,给出基于一定方式结合的框架与规则知识表示的推理机算法和规则推理机设计思想及实现方法,为学生选择学习内容和学习方法时对教学策略做出调整.     

Fuzzy knowledge representation and reasoning using a generalized fuzzy petri net and a similarity measure

In the study of weighted fuzzy production rules (WFPRs) reasoning, we often need to consider those rules whose consequences are represented by two or more propositions connected by “AND” or “OR”. To enhance the representation capability of those rules, this paper proposes two types of knowledge representation parameters, namely, the input weight and the output weight, for a rule. A Generalized Fuzzy Petri Net (GFPN) is also presented for WFPR reasoning. Furthermore, this paper gives a similarity measure to improve the evaluation method of WFPRs and the multilevel fuzzy reasoning in which the consequences and their certainty factors are deduced synchronously by using a GFPN.     

心电图的智能识别技术

模糊逻辑、神经网络是人工智能的重要分支,它们从不同角度、在一定程度上模拟了人类智能。本文先后将模糊逻辑、神经网络以及模糊神经网络技术用于心电图识别,获得了良好的效果。在模糊识别方面,从模糊识别矩阵的建立到模糊输入向量的确定,是针对此类具体问题的多传感器模糊信息融合算法,既综合考虑了各输入变量的作用,又突出了识别的主要依据。本文还给出了神经网络识别的三种试验结果及其与模糊神经网络识别的对比。模糊神经网络既充分发挥了神经网络的学习功能,又充分发挥了模糊逻辑的推理功能,因此具有很高的识别精度。     

脉冲GTAW熔池动态过程模糊神经网络建模与控制

展示了模糊推理与神经网络结合在脉冲GTAW熔池动态过程智能控制中的应用研究 结果.建立了脉冲GTAW平板对接动态过程特征:正反面熔池的最大宽度、长度与面积等参数 的神经网络模型,基于实验数据采用模糊辨识方法提取焊接过程的模糊控制规则,进而设计了 具有自学习适应能力的模糊神经网络控制器.建立了脉冲GTAW熔池动态过程智能控制系统, 焊接实验验证了所设计的模糊神经网络控制器具有智能控制效果.     

Data-driven discovery of quantitative rules in relational databases

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases     

一种基于神经网络的模糊推理和规则生成方法

文章介绍一种基于神经网络的模糊推理和规则生成方法,该方法在构造网络时能辨识网络结构和参数,且需要很少的先验信息;文章提出一种混合学习方法,该学习方法分两阶段进行学习,第一阶段使用一种改进的竞争学习方法,建立模糊规则。第二阶段,通过梯度下降技术,来优化模糊规则的参数,以达到高性能的模型。学习后的网络,模糊推理系统的参数融于在网络的拓扑中。文章还给出实验数据。     

基于递阶遗传算法模糊加权神经网络的模糊规则自动获取

针对模糊规则的自动获取一直是模糊系统的一个瓶颈问题，提出一种基于递阶结构的混合编码遗传算法与进化规划相结合的模糊加权神经网络学习新算法，利用该算法同时优化模糊加权神经网络的结构和参数，最后说明了从网络中提取模糊规则的方法，从而自动获得最优的模糊规则。分析和实验结果表明，本文方法在规则提取和分类准确性等方面比其他方法更好。