Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: A case study in cane farms in Iran

Development of a fuzzy inference model is a complex multi-step process in which we encounter a large number of parameters such as type and number of membership functions, fuzzy operators, defuzzification and implication methods and etc. There is currently very little literature on the topic of the best selection of parameters for development of expert based inference models. In this study we developed a fuzzy rule based model, which uses available farm management data as required inputs, for the environmental assessment of farming systems. We also tried to make an analysis on the efficiency of current mathematical parameters in the development of our fuzzy model. Finally, in a practical example we demonstrate the applicability of the developed model for improvement of environmental status of the cane farming in Iran.A Mamdani fuzzy inference model with two inference engines was developed to combine five basic input indexes, which were selected as indicators of farms environmental status based on the experts' interview and scientific knowledge. To validate the developed model, we inserted several cycles of analysis using graphical and global sensitivity methods on the model and compared the model outcomes with experts' viewpoints. Using these analysis methods, we also evaluated the effects of changes in operators, membership function shape and defuzzification methods, on the model outcomes and their sensitivities.In this study, fuzzy inference emerged as a suitable, uncomplicated and effective tool for development of environmental assessment models. Totally, performance of one parameter was highly influenced by other parameters. For the selection of one parameter its interaction with other parameters had to be considered. Type, shape and the number of membership functions were from the most effective parameters for development of the model and significantly influenced the other factors. Case study results showed that environmental indexes of sugarcane production can enhance between 37 and 59% using simple improving strategies.     

基于改进D-S证据和融合权集的安全风险评估

针对信息化系统安全风险评估过程中安全风险因素指标的重要性难以赋权的问题,本文以建筑工地施工现场为应用场景,提出一种基于改进的D-S证据理论与融合权集结合的安全风险评估模型.首先,在充分研究建筑工地安全风险评估流程和要素的基础上,建立建筑工地安全评价体系;其次,采用基于权值分配和矩阵分析的D-S合成算法改进AHP法和基于数据的熵权法计算评价体系中指标层中各指标的主、客观权重;然后,运用改进的D-S证据融合算法进行多源证据的合成,获取指标权重,避免单一赋权的片面性,得到最优综合权重;最后,根据TOPSIS评价算法计算建筑工地综合评价指数.分析表明,基于改进D-S证据理论-融合权集的安全风险评估模型,能有效评估建筑工地施工现场的安全性,降低评估结果的不确定性,提高风险评估结果可信度.     

基于ANFIS的交通事件持续时间预测

针对高速公路交通事故引发交通堵塞的问题,提出一种基于减法聚类和自适应神经模糊推理系统的事件持续时间预测新方法。将该方法应用于交通事件持续时间预测,从I-880数据库中提取事件持续时间相关因素,使用非参数估计法进行显著性分析,将影响程度最大的因素作为模糊系统的输入样本,采用减法聚类对输入样本进行聚类,得到模糊规则数并建立初始模糊推理系统,使用BP反向传播算法和最小二乘估计算法的混合算法对该模糊系统进行训练并优化,建立最终模糊模型。仿真结果证明,该系统对交通事件持续时间预测具有较高检测率和较低误报率。     

基于改进型模糊聚类的模糊系统建模方法

结合减法聚类和模糊C均值聚类,提出了一种改进型聚类算法,加快了收敛速度．利用改进后的算法对模糊系统输入或输出的样本集聚类,对聚类结果采用Trust-Region法拟合高斯型和S型函数,以实现模糊系统输入、输出空间的划分和隶属度函数参数的确定．结合MATLAB的模糊和曲线拟合工具箱,详述了如何在标准算法上进行改进和模糊系统建模．通过对IRIS标准数据聚类实验以及在解决机械加工误差复映问题上的应用,验证了改进后算法和建模方法的有效性．     

A method for the evaluation of risk in IT projects

Information technology projects are particularly prone to failure due to their specific characteristics, making risk management become one of the critical elements in IT projects management. That is why several authors have developed risk evaluation methods, some of them based on fuzzy logic. This article proposes a new risk assessment method based in a combination of fuzzy analytic hierarchy process (FAHP) and fuzzy inference system (FIS). FIS is used for the integration of the groups of risk factors. These risk factors are the evaluation criteria of a modified FAHP which minimizes the disadvantages of the classic implementation of FAHP in order to obtain a more intuitive and easily adjustable model for multicriteria decision analysis with a lower computational need. The proposed model takes into consideration the different levels of uncertainty, the interrelationship among groups of risk factors, and the possibility of adding or suppressing options without losing the consistency with previous evaluations. The new method is especially suitable for the evaluation of development projects in the area of IT in which multiple interrelated risk factors can be particularly uncertain and imprecise. To implement the evaluation method, a hierarchy of risk factors was implemented. A numerical example is presented with data from three actual cases of IT projects, showing the applicability of the new method, the suitability of the selected taxonomy, and the significance of a few risk factors. Several future lines of work are proposed.     

直觉模糊神经网络的函数逼近能力

运用直觉模糊集理论,建立了自适应神经-直觉模糊推理系统（ANIFIS）的控制模型,并证明了该模型具有全局逼近性质.首先将Zadeh模糊推理神经网络变为直觉模糊推理网络,建立一个多输入单输出的T-S型ANIFIS模型;然后设计了系统变量的属性函数和推理规则,确定了各层的输入输出计算关系,以及系统输出结果的合成计算表达式;最后通过证明所建模型的输出结果计算式满足Stone-Weirstrass定理的3个假设条件,完成了该模型的全局逼近性证明.     

Self-organizing neuro-fuzzy system for control of unknown plants

A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and the inference activities of the cluster-based SO-NFS. With the construction theory, a compact equation for describing the relation between the input base variables and inference results is established. This equation not only gives the inference relation between inputs and outputs but also specifies the linguistic meanings in the process. New pseudo-error learning control is proposed for closed-loop control applications. Using a cluster-based algorithm, the neuro-fuzzy system in its genesis can be generated by the stimulation of input/output training data to have its initial control policy (IF-THEN rules) for application. With the well-known random optimization method, the generated neuro-fuzzy system can learn its data base for specific applications. The proposed approach can be applied on control of unknown plants, and can levitate the curse of dimensionality in traditional fuzzy systems. Two examples are demonstrated.     

On the use of fuzzy synthetic evaluation and optimal classification for computing fire risk ranking of buildings

Fire risk ranking is particularly useful for building designers to compare two different solutions to assess if the safety is similar. However, the multi-criteria and imprecise nature of the fire safety attributes in buildings has caused difficulties in quantifying the fire safety level. Further to the previous works, the author presented a fuzzy synthetic evaluation system for computing the fire risk ranking of buildings. It could serve for multi-level fire safety assessment framework. Linguistic terms were adopted instead of subjective numerical values. A two-level evaluation model, including fuzzy optimal classification model and linear weighted mean model, was developed to facilitate the synthetic process. A case of fire safety ranking in high-rise residential buildings in Hong Kong was presented for illustration purpose. The evaluation result was analyzed by the maximum membership degree principle method.     

Development of a systematic methodology of fuzzy logic modeling

This paper proposes a systematic methodology of fuzzy logic modeling for complex system modeling. It has a unified parameterized reasoning formulation, an improved fuzzy clustering algorithm, and an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces 4 parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. The fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering. Major bottlenecks are addressed and analytical solutions are suggested. We also address the classification process to extend the derived fuzzy partition to the entire output space. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy fine clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples-a nonlinear function and a gas furnace dynamic procedure-are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches     

基于模糊集和DS证据理论的信息安全风险评估方法*

在信息安全风险评估过程中,存在着很多不确定和模糊的因素,针对专家评价意见的不确定性和主观性问题,提出了一种将模糊集理论与DS证据理论进行结合的的风险评估方法。首先,根据信息安全风险评估的流程和要素,建立风险评估指标体系,确定风险影响因素;其次,通过高斯隶属度函数,求出专家对各影响因素的评价意见隶属于各个不同评价等级的程度;再次,将其作为DS理论所需的基本概率分配,引入基于矩阵分析和权值分配的融合算法综合多位专家的评价意见;最后,结合贝叶斯网络模型的推理算法,得出被测信息系统所面临的风险大小,并对其进行分析。结果显示,将模糊集理论和DS证据理论应用到传统贝叶斯网络风险评估的方法,在一定程度上能够提高评估结果的客观性。     

A unified neurofuzzy model for classification

This work proposes a unified neurofuzzy modelling scheme. To begin with, the initial fuzzy base construction method is based on fuzzy clustering utilising a Gaussian mixture model (GMM) combined with the analysis of covariance (ANOVA) decomposition in order to obtain more compact univariate and bivariate membership functions over the subspaces of the input features. The mean and covariance of the Gaussian membership functions are found by the expectation maximisation (EM) algorithm with the merit of revealing the underlying density distribution of system inputs. The resultant set of membership functions forms the basis of the generalised fuzzy model (GFM) inference engine. The model structure and parameters of this neurofuzzy model are identified via the supervised subspace orthogonal least square (OLS) learning. Finally, instead of providing deterministic class label as model output by convention, a logistic regression model is applied to present the classifier’s output, in which the sigmoid type of logistic transfer function scales the outputs of the neurofuzzy model to the class probability. Experimental validation results are presented to demonstrate the effectiveness of the proposed neurofuzzy modelling scheme.     

基于改进模糊聚类与ANFIS的高速公路事件检测

为了准确并及时地发现高速公路上的交通事故隐患,减少事故引发的交通延迟,提高高速公路运行安全性,结合减法聚类与模糊C均值（FCM）聚类算法对输入样本数据进行聚类,建成初始模糊推理系统,然后通过神经网络的自学习机制,训练模糊系统参数,确定模糊推理规则,建立最终模糊模型。通过仿真实验结果对比,验证了基于改进模糊聚类与自适应神经模糊推理系统（ANFIS）建模方法的有效性。     

基于云模型的列控中心行车许可功能的安全风险评估

为解决列控中心（TCC）风险评估过程中的随机性和模糊性,提高信号系统风险评估的科学性,采用云模型和模糊层次分析法评估列车行车许可功能的风险等级。利用模糊层次分析法确定列车行车许可功能的风险因素集及权重值,考虑到行车许可功能风险影响因子的模糊性,借助云模型对定性的风险因子量化转换,通过云模型定量评价影响风险因子指标的风险等级情况。最后从定性和定量的角度分析列车行车许可功能的风险等级。结果表明：云模型和模糊层次分析法得到的综合云模型和标准云模型分布对比,客观地表示了风险等级状况,验证评价结果符合实际状况。     

Image restoration techniques based on fuzzy neural networks

By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter-FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.     

阔大货物装载加固决策推理方法研究

阔大货物装载加固决策是一个半结构化问题,绝大多数装载加固参数无法直接经由传统的数学模型求解得到,采用科学合理的货物装载加固方案,对降低阔大货物的运输成本和确保货物按时安全运达具有十分重要的意义。综合考虑阔大货物装载加固各种影响因素及其安全、技术条件,运用物元理论,刻画并深入分析阔大货物装载加固实例模型的结构要素及其典型特征;采用基于实例的推理思路,构建初次评价条件集合和实例匹配条件集合,设计实例匹配优度评价法,评价待解实例与实例集中各实例之间的相似程度,提出基于实例的阔大货物装载加固决策推理算法。给出具体的装载加固演算实例,进一步验证了所提出的决策推理方法的合理性和有效性。     

Monitoring defects of ceramic tiles using fuzzy subtractive clustering-based system identification method

In this paper, a subtractive clustering fuzzy identification method and a Sugeno-type fuzzy inference system are used to monitor tile defects in tile manufacturing process. The models for the tile defects are identified by using the firing mechanical resistance, water absorption, shrinkage, tile thickness, dry mechanical resistance and tiles temperature as input data, and using the concavity defect and surface defects as the output data. The process of model building is carried out by using subtractive clustering in both the input and output spaces. A minimum error model is developed through exhaustive search of clustering parameters. The fuzzy model obtained is capable of predicting the tile defects for a given set of inputs as mentioned above. The fuzzy model is verified experimentally using different sets of inputs. This study intends to examine and deal with the experimental results obtained during various stages of ceramic tile production during 90-day period. It is believed, that the results obtained from the present study could be considered in other ceramic tiles industries, which experienced similar forms of defects.     

Fuzzy inference to risk assessment on nuclear engineering systems

This paper presents a nuclear case study, in which a fuzzy inference system (FIS) is used as alternative approach in risk analysis. The main objective of this study is to obtain an understanding of the aging process of an important nuclear power system and how it affects the overall plant safety. This approach uses the concept of a pure fuzzy logic system where the fuzzy rule base consists of a collection of fuzzy IF–THEN rules. The fuzzy inference engine uses these fuzzy IF–THEN rules to determine a mapping from fuzzy sets in the input universe of discourse to fuzzy sets in the output universe of discourse based on fuzzy logic principles. The risk priority number (RPN), a traditional analysis parameter, was calculated and compared to fuzzy risk priority number (FRPN) using scores from expert opinion to probabilities of occurrence, severity and not detection. A standard four-loop pressurized water reactor (PWR) containment cooling system (CCS) was used as example case. The results demonstrated the potential of the inference system for subsiding the failure modes and effects analysis (FMEA) in aging studies.     

有混合数据输入的自适应模糊神经推理系统

现有数据建模方法大多依赖于定量的数值信息,而对于数值与分类混合输入的数据建模问题往往根据分类变量组合建立多个子模型,当有多个分类变量输入时易出现子模型数据分布不均匀、训练耗时长等问题.针对上述问题,提出一种具有混合数据输入的自适应模糊神经推理系统模型,在自适应模糊推理系统的基础上,引入激励强度转移矩阵和结论影响矩阵,采用基于高氏距离的减法聚类辨识模型结构,通过混合学习算法训练模型参数,使数值与分类混合数据对模糊规则的前后件参数同时产生作用,共同影响模型输出.仿真实验分析了分类数据对模型规则后件的作用以及结构辨识算法对模糊规则数的影响,与其他几种混合数据建模方法对比表明本文所提出的模型具有较高的预测精度和计算效率.     

Assessing airborne transmission of foot and mouth disease using fuzzy logic

In case of an outbreak of foot and mouth disease, the prediction of airborne spread is an important tool for decision-makers to assess the potential risk of secondary infections. Modelling approaches such as the Gaussian dispersion or Lagrangian particle model have been established but are complex to use and the structure of the models is fixed rather than adjustable to emerging disease situations. The aim of the present study was to evaluate the application of fuzzy logic as a modelling technique based on linguistic variables. Fuzzy logic models are easy to use and to modify. Adaptations to emerging outbreaks seem feasible. Using the Gaussian dispersion model as a reference, livestock-specific fuzzy logic models were developed. In a stepwise modelling process, the input parameters of the Gaussian model were added one-by-one into the fuzzy models. On the basis of weather data and randomly allocated farms, a validation dataset with 10,000 observations was generated and used in a 10-fold cross validation to compare the two modelling approaches. A good agreement between the Gaussian dispersion and the fuzzy logic models concerning the main directions of virus spread were found. The measure of agreement ranged between 87.0% and 99.9%. Falsely classified observations occurred mostly in proximity to the boundary of virus transmission based on the Gaussian dispersion model. In conclusion, fuzzy logic determined the same risk of infection for secondary cases than the Gaussian dispersion model. Limitations to certain livestock were not observed. The inclusion of up to four input variables did not influence the results in a mentionable amount. Including additional input variables into the fuzzy models could improve its application in assessing the risk of airborne foot and mouth disease transmission furthermore.     

Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic

Artificial neural networks and fuzzy logic approaches have recently been used to model some of the human activities in many areas of civil engineering applications. Especially from these systems in the model experimental studies, very good results have been obtained. In this research, the models for predicting compressive strength of mortars containing metakaolin at the age of 3, 7, 28, 60 and 90 days have been developed in artificial neural networks and fuzzy logic. For purpose of building these models, training and testing using the available experimental results for 179 specimens produced with 46 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models are arranged in a format of five input parameters that cover the age of specimen, metakaolin replacement ratio, water–binder ratio, superplasticizer and binder–sand ratio. According to these input parameters, in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models, the compressive strength of mortars containing metakaolin are predicted. The training and testing results in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models have shown that neural networks and fuzzy logic systems have strong potential for predicting compressive strength of mortars containing metakaolin.