Session Program


  • 11 July 2017
  • 04:00PM - 06:00PM
  • Room: Normanna
  • Chairs: Christian Wagner and Jon Garibaldi

Type-2 Fuzzy Sets and Systems Applications-II

Abstract - Traffic congestion leads to many problems, namely road users' dissatisfaction, air pollution and waste of time and fuel. For this reason, congestion detection at an early stage is required to perform an efficient exploitation of resources. This paper proposed a Hierarchical Type-2 Beta Fuzzy Knowledge Representation system for the selection of optimal route. Consequently, this system aims to avoid longer travel times, and to decrease traffic accidents and the number of traffic congestion situations. The selection is performed through itineraries assessment by contextual factors such as Max speed and density of a given path. For the validation, the traffic simulation was done with the open source microscopic road traffic simulator SUMO. When compared with the Dijkstra's algorithm, the proposed system showed better performance in terms of average travel time and path flow. These promising results prove the potential of our method to relieve traffic congestion.
Abstract - A switch machine is an electromechanical device that allows railway trains to be guided from one track to another. Among all possible faults that can occur in a switch machine, the three mains ones are: lack of lubrication, lack of adjustment and malfunction of a component. Aiming to classify these faults, an important contribution of this work is to address the height type-reduction and interval singleton type-2 fuzzy logic system derivatives. The computational simulations are performed with real data set provided by a Brazilian company of the railway sector. The obtained results are compared with other models reported in the literature (Bayes theory, multilayer perceptron neural network and type-1 fuzzy logic system), demonstrating the effectiveness of the proposed classifier and revealing that the proposal is able to properly handle with uncertainties associated with the measurements and with the data that are used to tune the parameters of the model. In addition, the convergence speed and performance analysis show that the proposed interval singleton type-2 fuzzy logic system is attractive for classifying faults in a switch machine.
Abstract - Conventional fuzzy time series approaches make use of type-1 or type-2 fuzzy models. Type-1 models with one index (membership grade) cannot fully handle the level of uncertainty inherent in many real world applications. The type-2 models with upper and lower membership functions do handle uncertainties in many applications better than its type-1 counterparts. This study proposes the use of interval type-2 intuitionistic fuzzy logic system of Takagi-Sugeno-Kang (IT2IFLS-TSK) fuzzy inference that utilises more parameters than type-2 fuzzy models in time series forecasting. The IT2IFLS utilises more indexes namely upper and lower non-membership functions. These additional parameters of IT2IFLS serve to refine the fuzzy relationships obtained from type-2 fuzzy models and ultimately improve the forecasting performance. Evaluation is made on the proposed system using three real world benchmark time series problems namely: Santa Fe, tree ring and Canadian lynx datasets. The empirical analyses show improvements of prediction of IT2IFLS over other approaches on these datasets.
Abstract - This paper proposes a new accuracy evaluation method within a behavioral comparison strategy which uses interval type-2 fuzzy sets and derived operations to model reference data and define soft accuracy indexes. The method addresses the case in which grades of membership, collected by surveying experts, will often be different for the same reference pattern, because the experts will not necessarily be in agreement. The approach is illustrated using simple examples and an application in the domain of biomedical image segmentation.
Abstract - In this paper, the glucose regulation system is identified by interval type-2 fuzzy neural network based on genetic algorithm used to adapt the model parameters. The interval type-2 fuzzy neural network is constructed to identify the glucose regulation system of the people with diabetes type-1. The centers and widths of the memberships and output weights of the interval type- 2 fuzzy neural network can be tuned by optimizing genetic algorithm. The simulation result shows that the glucose-insulin behavior can be well identified by the advocated identification scheme
Abstract - In this paper, a novel control scheme for the flexible air-breathing hypersonic vehicle (FAHV) using adaptive interval type-2 fuzzy logic system (AIT2-FLS) is proposed to reduce the side effects of measurement noises in the velocity channel and altitude channel as well as flexible dynamics in real applications. After input-output linearization of the longitudinal model of FAHV, the dynamic inversion controller is formulated to track the reference commands based on state feedback. The AIT2-FLS is further developed to deal with the model uncertainties and input errors. Besides, the state estimator is applied to estimate the true values of the corrupted outputs. The stability characteristics of both the controller and the state estimator are analyzed. The whole control scheme is finally obtained through combining the controller and the state estimator based on the separation principle. Simulation results demonstrate the robustness of our proposed control scheme against measurement noises and flexibilities.