Fuzzy Reasoning Model Research Articles

Application of fuzzy logic and neural networks for dynamic travel time estimation

AbstractThe effectiveness of loop detectors as a data source for advanced traveler information systems has been researched recently [V. P. Sisiopiku (1993 Travel Time Estimation from Loop Detector Data for Advanced Traveler Information System Applications, Ph.D. Thesis, University of Illinois at Chicago]. In urban traffic control schemes loop detectors provide on‐line information on traffic conditions consisting of volume counts and occupancy levels. The need to convert loop detector data into travel times is recognized mostly in data fusion applications [P. Nelson and P. Palacharla (1993) A neural network model for data fusion in ADVANCE, Pacific Rim Transportation Technology Conference Proceedings, Vol. I, pp. 237–243, Seattle, WA, 1993]. Literature review indicates limited knowledge on the actual relationship between travel times and loop detector data under interrupted traffic conditions [V. P. Sisiopiku and N. M. Rouphail (1994) Towards the Use of Detector Output for Arterial Link Travel Time Estimation: a Literature Review. Transportation Research Record Series, Washington, DC]. Currently available statistical regression models cannot capture the dynamics of traffic conditions under signalized control and suffer from limited calibration and empirical validation. This paper presents a fuzzy reasoning model to convert loop detector data into link travel times obtained from empirical studies. This model incorporates flexible reasoning and captures non‐linear relationship between link specific detector data and travel times.

Application of fuzzy logic and neural networks for dynamic travel time estimation

The effectiveness of loop detectors as a data source for advanced traveler information systems has been researched recently [V.P. Sisiopiku, (1993) Travel Time Estimation from Loop Detector Data for Advanced Traveler Information Systems Applications, Ph.D. Thesis, University of Illinois at Chicago]. In urban traffic control schemes loop detectors provide on-line information on traffic conditions consisting of volume counts and occupancy levels. The need to convert loop detector data into travel times is recognized mostly in data fusion applications [ Palacharla and Nelson, (1995)A neural network model for data fusion in ADVANCE, Pacific Rim Transportation Technology Conference Proceedings, Vol. I, pp. 237–243, Seattle, WA, 1993]. Literature review indicates limited knowledge on the actual relationship between travel times and loop detector data under interrupted traffic conditions [ Sisiopiku et al., (1994b)Towards the Use of Detector Output for Arterial Link Travel Time Estimation: a Literature Review. Transportation Research Record Series, Washington, DC]. Currently available statistical regression models cannot capture the dynamics of traffic conditions under signalized control and suffer from limited calibration and empirical validation. This paper presents a fuzzy reasoning model to convert loop detector data into link travel times obtained from empirical studies. This model incorporates flexible reasoning and captures non-linear relationship between link specific detector data and travel times.

A fuzzy approximate reasoning model for a rule-based system in laser threat recognition

Intrinsically, vague knowledge is typical of the decision process especially when it is based on complex and non-numeric data. Linguistic descriptions play a significant role in decision making for pattern recognition. Fuzzy set theory and fuzzy logic provide an approximate framework for the representation of imprecise concepts and imprecise models of reasoning. In this article, fuzzy logic is proposed as a means of handling uncertainty in an expert system structure for automatic laser threat recognition which is increasingly required in modern war. Fuzzy characteristics of the feature values of laser threat patterns are comprehensively described. A prototype system has been developed and tested on labeled sample data of laser threats. The results are compared to our previous work, a similar recognition system.

A neurofuzzy approach to variational problems by using Gaussian membership functions

In this paper we propose a neurofuzzy direct solution method for variational problems in which the cost function of an integral form is minimized. We deal with two nonlinear systems; one is a direct drive (DD) manipulator systems, and the other is a trailer-truck system. The DD manipulator system is described by a continuous-time dynamical model, and the trailer-truck system is described by a discrete-time dynamical model. The problem is to find trajectories which minimize the cost function of an integral form. The trajectories of state variables and input variables are represented by fuzzy models that consists of Gaussian membership functions. The networks of Gaussian functions are trained by the steepest-descent method to minimize the cost function. The proposed neurofuzzy approach provides a direct solution method of the variational problems by using Gaussian functions. The function is regarded as a simplifie fuzzy reasoning model and called neurofuzzy.

Constructing fuzzy model by self-organizing counterpropagation network

This paper describes a general and systematic approach to constructing a multivariable fuzzy model from numerical data through a self-organizing counterpropagation network (SOCPN). Two self-organizing algorithms USOCPN and SSOCPN, being unsupervised and supervised respectively, are introduced. SOCPN can be employed in two ways. In the first place, it can be used as a knowledge extractor by which a set of rules are generated from the available numerical data set. The generated rule-base is then utilized by a fuzzy reasoning model. The second use of the SOCPN is as an online adaptive fuzzy model in which the rule-base in terms of connection weights is updated successively in response to the incoming measured data. The comparative results on three well studied examples suggest that the method has merits of simple structure, fast learning speed, and good modeling accuracy.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Neuro-fuzzy GMDH and its application to modelling grinding characteristics

Mathematical models, in which many input variables are involved, require a range of input and output data, since the number of parameters increases with the input variables. GMDH (Group Method of Data Handling) has been used for the identification of a mathematical model that has many input variables but limited data needs by using a hierarchical structure. This paper proposes a neuro-fuzzy GMDH model, adopting Gaussian radial basis functions (GRBF) as partial descriptions of GMDH. GRBF is reinterpreted as both a simplified fuzzy reasoning model and as a three-layered neural network. As an example of applying the algorithm, the wheel wear equation is identified, using data from experiments of abrasive cut-off. In the model, characteristics of work materials, grinding fluids, factors of wheels, wheel velocity and table feed are used as input variables, and the grinding ratio is the resulting output. The validity of the model is confirmed within the predicted accuracy by using additional data.

A new fuzzy reasoning model for decision support system : Xiaozhe Zhao, Zhongtuo Wang, pp 803–806

A new fuzzy reasoning model for decision support system : Xiaozhe Zhao, Zhongtuo Wang, pp 803–806

Genetic-based new fuzzy reasoning models with application to fuzzy control

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown in this paper that the performance of fuzzy control systems may be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller. >

A neuro-fuzzy approach to data analysis of pairwise comparisons

Artificial neural networks provide iterative on-line learning schemes for modeling non-linear systems. An iterative learning algorithm in fuzzy models, which is called neuro-fuzzy, has been recently developed within the framework of fuzzy modeling in the sense of M. Sugeno. In this paper, using neuro-fuzzy approach, two quantification methods of pairwise comparisons are presented in order to derive the associated weights of different objects. The proposed methods can be applied even in the case of incomplete pairwise comparisons. A simplified fuzzy reasoning model is obtained in the form of Gaussian radial basis functions. The psychological sensation responses of human beings to minute vibrations are analyzed by the newly proposed neuro-fuzzy approach. The proposed approach is compared with Guttman's method and Saaty's analytic hierarchy process (AHP). In our two neuro-fuzzy approaches, psychological values are obtained with the interval and the ratio scale properties. They are represented by smooth functions of class C ∞.

A New Fuzzy Reasoning Model for Decision Support System

This paper proposes a new fuzzy reasoning model, involved the knowledge representation sub–model based on synthetic language system, the operators and the inference rules it uses, and, the meta–rules for the firing rule selection. The model is used for the knowledge – based decision support to make DSS enable to support human decision making process in which information coming from real – world is imperfect. The new model is a combination of fuzzy production model based on Zadeh's fuzzy logic and traditional production model based on traditional two–valued or multi–valued logic. The new model is efficient in inference with fuzzy information by means of the combining the inference rules of fuzzy logic with the variety of the matching rules of the traditional production systems and the quantifier – tables, make it possible for explaining the inference process and inference consequences, that is, the decision support process.

A Comparative Study on Estimation Models of Diversion Rate with Fuzzy Set Theory

Estimation model of diversion rate has been proposed for traffic demand forecasting on urban expressway. Fuzzy reasoning model was already proved to be useful to describe human behavior. The major objective of the study is to introduce somekinds of methods with fuzzy reasoning and to make their feature obvious. First, diversion model is developed with actual survey data considering the reality in traffic assignment stage. Second, implication methods on t-norms for reasoning are compared to know their merits of description and limitation of estimation. Third, the product-sum composition is investigated as a newly idea to modify conventional fuzzy reasoning methods from logical and practical point of view.

Traffic control process of expressway by fuzzy logic

There are many complicated and fuzzy situations in the case of controlling the traffic flow on an urban expressway. Data obtained by traffic detectors and information about other roads all should be considered to determine the best operation. The control of an urban expressway, therefore, has been dependent upon skilled operators' judgement and decision. The objective of the study reported here is to describe the judgement process of the operator by fuzzy logic and to suggest a traffic control system that could determine the pattern of on-ramp control of the expressway automatically. Establishing a fuzzy logic traffic controller according to a real operation process would be valuable to decrease the work load of the skilled operators who make decisions every time, and helpful to develop a more effective operation procedure. In this study, a simple fuzzy reasoning model for on-ramp control is constructed. The actual operating data were gathered on Hanshin Expressway. The Osaka-Sakai route is the example route on which the performance of the model is examined. As a result of the study, it becomes clear that the fuzzy logic traffic controller system is effective to describe and take the place of human operators. In addition, improvement of the model and a total system of fuzzy traffic control is suggested.

あいまい推論を用いた避航操船モデル

あいまい推論を用いた避航操船モデル