Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).     

Automatic control of biomass gasifiers using fuzzy inference systems

A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.     

A genetic-algorithm-based approach to optimization of bioprocesses described by fuzzy rules

A new approach to optimization of bioprocesses described by fuzzy rules is introduced in the paper. It is based on genetic algorithms (GA) and allows to determine optimal values or profiles of control variables and to optimize fuzzy rules (parameters of membership functions). The process can be described by linguistic variables and fuzzy rules. An algorithm and related software was developed. The approach was applied to an industrial antibiotic fermentation. The optimal profile of a physical variable of the preculture was determined which leads to an increasing output product concentration in the main culture of about 5%.     

基于CVM的海涂湿地生态服务价值的模糊评估模型

正确评估海涂湿地生态服务价值有助于加强人们保护海涂湿地的意识,为海涂湿地围垦生态补偿标准的制定提供依据。视价格"便宜"、"适中"和"昂贵"为模糊集,基于CVM法区间型数据建立了传统模糊统计模型、赋权模糊统计模型和三相划分模型,并以此评价了杭州湾国家湿地公园单位面积年生态系统服务价值。结果显示:3种方法得到的单价分别为10.28、10.38、9.76元m~(-2)a~(-1),三者一致程度较高,与国内沿海湿地价值比较接近。分析了"生态价值"概念客观上的"模糊性"引致模糊数学模型的合理性;采用"橄榄球"式赋权建立赋权模糊统计模型以克服传统模糊统计模型中对区间数据均匀赋权的不合理性,结果在隶属度函数的光滑性和拟合度上好于后者;第三,三相划分模型拟合出3个模糊集的隶属度函数,可以比较相同价格在不同模糊集中隶属度差异。建立的模型对于基于CVM法的生态资源价值评估具有借鉴意义。     

Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocesses

Summary An object-oriented fuzzy expert system to support on-line control of an automated fermentation plant is described. The major elements of the system consist of a fuzzy inference engine, a database, a knowledge base, and an expression evaluater. The expression evaluater calculates specific rates for growth, and substrate and product formation at different physiological states during the cultivation from the measured data. The specific rates are then compared with the standard target rates stored in the database. If differences outside the set tolerances were observed, the inference engine analyses the reasons for the faults on the basis of the knowledge represented in the form of a knowledge network and fuzzy membership functions of the process variables. The fuzzy expert system was developed on the basis of a shell constructed by using the object oriented Smalltalk/V Mac programming environment, with Lactobacillus casei lactic acid fermentation as the example of process application.Visiting scientist from Helsinki University of Technology at RIKEN Correspondence to: P. Linko or I. Endo     

Fuzzy logic control of a fed-batch fermentor

A rule-based fuzzy logic control is developed for control of penicillin concentration in a fed-batch bioreactor. The membership functions, fuzzy ranges for the error and for the controller output are defined. A fuzzy rule base is constructed relating error to the control output based on operators' knowledge. The performance of the fuzzy-logic controller is evaluated by simulating a mathematical model of the fed-batch bioreactor.     

APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS

This paper describes an initial but fundamental attempt to lay some groundwork for a fuzzy-set-based paradigm for sensory analysis and to demonstrate how fuzzy set and neural network techniques may lead to a natural way for sensory data interpretation. Sensory scales are described as fuzzy sets, sensory attributes as fuzzy variables, and sensory responses as sample membership grades. Multi-judge responses are formulated as a fuzzy membership vector or fuzzy histogram of response, which gives an overall panel response free of the unverifiable assumptions implied in conventional approaches. Neural networks are used to provide an effective tool for modeling and analysis of sensory responses in their naturally fuzzy and complex forms. A maximum method of defuzzification is proposed to give a crisp grade of the majority opinion. Two applications in meat quality evaluation are used to demonstrate the use of the paradigm and procedure. It is hoped that this work will bring up some new ideas and generate interest in research on application of fuzzy sets and neural networks in sensory analysis.     

A method for solution of the multi-objective inverse problems under uncertainty

We describe a method to solve multi-objective inverse problems under uncertainty. The method was tested on non-linear models of dynamic series and population dynamics, as well as on the spatiotemporal model of gene expression in terms of non-linear differential equations. We consider how to identify model parameters when experimental data contain additive noise and measurements are performed in discrete time points. We formulate the multi-objective problem of optimization under uncertainty. In addition to a criterion of least squares difference we applied a criterion which is based on the integral of trajectories of the system spatiotemporal dynamics, as well as a heuristic criterion CHAOS based on the decision tree method. The optimization problem is formulated using a fuzzy statement and is constrained by penalty functions based on the normalized membership functions of a fuzzy set of model solutions. This allows us to reconstruct the expression pattern of hairy gene in Drosophila even-skipped mutants that is in good agreement with experimental data. The reproducibility of obtained results is confirmed by solution of inverse problems using different global optimization methods with heuristic strategies.     

Fuzzy—CRI法对砀山酥梨丰产树形结构的探讨

本文根据Fuzzy-CRI近似推理方法,建立了文中图1的Fuzzy推理系统模型,分别对现有不同类型砀山酥梨树形结构的丰产性能作定量研究,并从中得到了一种理想的树形结构模型．针对本文背景,给定了隶属函数与决策函数的表达形式,提出并讨论了一种新的Fuzzy蕴涵算子．     

Hybrid modeling approach to on-line estimation of yeast biomass concentration in industrial bioreactor

The industrial fed-batch yeast cultivation process has been divided into four different metabolic phases (adaptation, carbon limited, oxygen limited and maturation) by a neuro-fuzzy classification model that consists of 4 applied linguistic rules on 2 state variables: oxygen uptake rate and liquid volume. The membership functions have been automatically adapted by this fuzzy perceptron, i.e., by a supervised learning algorithm initialized by prior operator's knowledge. Process compartmentalization has made easier and more realistic a subsequent state estimation of the biomass concentration with separate artificial neural networks combined with balance equations. Static networks with local recurrent memory structures were used, and the inputs were standard cultivation state variables: respiratory quotient, molasses feed rate, ethanol concentration, etc. This hybrid approach is generally applicable to state estimation or prediction when different sources of process information and knowledge have to be integrated.     

矿区生态产业评价指标体系

论述了煤炭矿区生态产业评价指标体系设置的理论依据,针对煤炭矿区生态产业链的特点和发展模式,从自然资源、经济效益、环境效益和社会效益四个角度出发选择筛选指标,构建了煤炭矿区生态产业评价指标体系.建立了模糊综合评价模型,探讨了运用层次分析法并通过熵值法修正的确定评价指标权重的新方法,构建了各因素指标的模糊隶属度函数.应用龙口矿区2010年的指标数据对所建模型和方法进行检验,对龙口矿区生态产业发展及生态产业链延伸提出了具体建议.     

Genetic algorithm based fuzzy logic control of a fed-batch fermentor

In the normal fuzzy logic control (FLC) system, both the membership functions and the rule sets are usually decided upon subjectively, case by case. The application of Genetic Algorithm(GA) could lead to proper selection of membership functions and rule base objectively. In this paper, the optimisation of membership functions of a FLC for a fed-batch fermentor is carried out with help of Genetic Algorithm (GA). Results are found to be satisfactory.     

On-line state recognition in a yeast fed-batch culture using error vectors

The physiological states with respect to cell growth and ethanol production in a yeast fed-batch culture expressed in linguistic form could be recognized on-line by fuzzy inferencing based on error vectors. The error vector was newly defined here in a macroscopic elemental balance equation. The physiological states for cell growth and ethanol production were characterized by error vectors using many experimental data from fed-batch cultures. Fuzzy membership functions were constructed from the frequency distributions of the error vectors and state recognition was performed by fuzzy inferencing. In particular, an unusual physiological state for a yeast cultivation, in which aerobic ethanol production was accompanied by very low cell growth, could be recognized accurately. According to the results of the state recognition, an energy parameter, the P/O ratio in the metabolic reaction model was adaptively estimated, and the cell growth was successfully evaluated with the estimated P/O. (c) 1995 John Wiley & Sons, Inc.     

Gene clustering based on clusterwide mutual information.

Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on minimizing mutual information among gene clusters. Simulated annealing is employed to solve the optimization problem. Bootstrap techniques are employed to get more accurate estimates of mutual information when the data sample size is small. Moreover, we propose to combine the mutual information criterion and traditional distance criteria such as the Euclidean distance and the fuzzy membership metric in designing the clustering algorithm. The performances of the new clustering methods are compared with those of some existing methods, using both synthesized data and experimental data. It is seen that the clustering algorithm based on a combined metric of mutual information and fuzzy membership achieves the best performance. The supplemental material is available at www.gspsnap.tamu.edu/gspweb/zxb/glioma_zxb.     

人工湿地生态系统服务综合评价研究

人工湿地作为一种高生产力的生态系统能为人类提供多种服务。但多年来对人工湿地的研究主要集中在其净化机理上, 而一直缺乏系统、综合的人工湿地生态系统服务评价体系。基于层次分析法和模糊隶属函数法等数学理论, 首次建立了对人工湿地生态系统服务进行综合评价的方法——人工湿地生态系统服务综合指数。开展了以北京奥林匹克森林公园人工湿地为例生态系统服务综合评价。其生态系统服务综合指数的得分为0.7848 分。该得分较理想, 说明北京奥林匹克森林公园人工湿地具有可观的生态系统服务价值。它能在净化水质、提供栖息地、有机质生产、微气候调节、休闲娱乐和科研教育等诸多方面提供良好服务。对人工湿地生态系统服务进行综合评价, 有利于比较不同人工湿地或同一人工湿地不同时期服务质量的优劣, 从而为人工湿地的研发、设计、建设、运行和管理提供指导。       

Mapping historical forest types in Baraga County Michigan,USA as fuzzy sets

Data on tree location and species in a portion of Northern Michigan were gathered from General Land Office (GLO) survey notes (ca. 1850), digitized, and generalized to represent forest types. Fuzzy membership values describing the degree of membership of each species in each forest type were derived from (a) semantic information in the forestry literature and (b) a fuzzy clustering routine applied to data from randomly placed circular plots. The fuzzy membership values assigned to each tree point for each forest type were interpolated to form continuous surfaces using kriging and co-kriging. Advantages of this method over traditional discrete mapping methods include: (a) multiple options are available for the display and analysis; (b) classification uncertainty and the continuity of natural vegetation can be represented; and (c) the classification scheme is applied systematically across the entire map area and can be altered to produce alternative maps. The subset of available display and analytical products presented include: discrete forest type maps; a surface representing the confusion between forest types; fuzzy logical overlays of forest types; and discrete class maps with color value altered within each class to indicate degree of confusion at each location.     

土壤模糊隶属度不同数据转换方法及其对空间插值结果的影响

应用模糊c-均值算法对土壤进行连续分类时,其输出的土壤模糊隶属度值具有成分数据的结构特点.直接基于土壤隶属度数据实施普通克里格插值,其空间预测结果缺乏可信度.因此,在进行插值预测之前,必须对土壤模糊隶属度值进行必要的数据转换.研究采用对数正态变换方法、对称对数比转换方法和非对称对数比转换方法对土壤模糊隶属度值进行数据转换,分析了各种数据转换形式对插值结果及其精度的影响.结果表明,对样点土壤模糊隶属度进行简单对数正态转换,其插值结果空间上任意点的土壤对于不同类别的隶属度之和均不为1,因此这样的插值结果理论上缺乏可行性.数据经非对称对数比转换和对称对数比转换后,插值结果均满足各个位置组分之和为1和非负限制,二者相比,后者对区域总体趋势的反映较前者好,且精度较高.因此,在应用对称对数比方法对样点土壤模糊隶属度值进行数据转换的基础上,应用克里格技术实施空间插值可以获得最佳预测结果.     

Accelerated search for biomolecular network models to interpret high-throughput experimental data

Background  

The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.     

Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process

Abstract

To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model’s adaptive abilities in various operation conditions and can improve its generalization ability.     

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.