this paper presents a fuzzy approach to the prediction of highly nonlinear timeseries.the optimized mamdani-type fuzzy system denoted sqp-flc is applied forthe input-output modeling of measured data. in order to tune fuzzy membershipfunctions, a sequential quadratic programming (sqp) method is employed. theproposed method is evaluated and validated on a highly complex time series, dailygold pri...

Linguistic modeling of complex irregular systems is helpful for the generation of decision making controls. In the various existing Fuzzy models, proposed by Mamdani, Sugeno, and Tsukamoto, the concepts of the set of membership functions and different Fuzzy logic rules to reason about data were addressed. The time control issues were not discussed in these models. In this paper, a new model is ...

Qualitative modeling may be applied when knowledge about a system is only available in linguistic form. The knowledge might be processed by a dynamic fuzzy system consisting of a rule base and an inference method modeling human reasoning. Conventional fuzzy inference methods do not consider this association to human reasoning and therefore are not suitable for the dynamic processing of linguist...

Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but ...

In this paper, a novel generalized predictive control (GPC) strategy using multiple models approach has been presented. The proposed strategy is realized based on the Takagi-Sugeno-Kang (TSK) fuzzy-based modeling for control of a tubular heat exchanger system. In this strategy, different operating environments of the system with varying parameters are first identified. Then for each environment...

This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in...

Takagi–Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear inputoutput submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear...

This paper includes a simulation model built in order to predict the performance indicessuch aswaiting time by analyzing queue’s components in the real world under uncertain and subjective situation. The objective of this paper is to predict the waiting time of each customer in an M/M/C queuing model. In this regard, to enable decision makers to obtain useful results with enough knowledge on th...

This paper describes the fuzzy modeling of permanent magnet generator for studying its mechanical dynamic analysis. Firstly electromagnetic torque analysis of the generator is carried out using finite element based package. Then fuzzy model of the generator is developed. Performance was evaluated by comparing, integrated fuzzy model, individual fuzzy model and finite element model for the gener...

background : drivers are vulnerable to musculoskeletal and psychological disorders because of substantially harmful agents in this stressful occupation. this study aims to investigate the influence of driver’s physical and psychological health on the risk of road accidents using fuzzy logic approach. methods : two input variables including musculoskeletal disorders (msds) and mental health, alo...