In this study, a black box modeling of the coupledtank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutiona...

The major work of intrusion detection systems is used to detect the anomaly and new attackers in the networks, even still various false alarms are caused in order to neglect this necessary feature. Existing system present an anomaly-based intrusion detection system to improve the system performance. Fuzzy rule-based modeling and fuzzy controller are used to create a detection model in the train...

Modeling human operator’s behavior as a controller in a closed-loop control system recently finds applications in areas such as training of inexperienced operators by expert operator’s model or developing warning systems for drivers by observing the driver model parameter variations. In this research, first, an experimental setup has been developed for collecting data from human operators as th...

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuz...

Fuzzy modeling has been widely applied as a powerful methodology for the identification of nonlinear systems from process measurements. In particular, the design of black-box approaches based on fuzzy models has been recognized as an alternative to mathematical methods. This article deals with the application of modeling and identification techniques for obtaining two fuzzy models of a solar do...

Since liquid tank systems are commonly used in industrial applications, system-related requirements results in many modeling and control problems because of their interactive use with other process control elements. Modeling stage is one of the most noteworthy parts in the design of a control system. Although nonlinear tank problems have been widely addressed in classical system dynamics, when ...

This paper extends the Prony method to the identification of a class of fuzzy systems parameterized by a set of time-varying parameters. The Prony formulation is an optimal solution minimizing the fluctuation effects while accurately estimating the time-varying system parameters. A closed-form and efficient algorithm for adaptively calculating the time-varying parameters is derived. This soluti...

accurate analysis and interpolation of heavy metals, especially arsenic concentrationin ground watercan play a significant role in planning and continuous monitoring of water resources. the analysis may also prevent human health issues.the purpose of this paper was to evaluate newly published sugeno type fuzzy inference system as an interpolation method for estimating the amount of arsenic in k...

This paper introduces a new method to control resonance frequency and output power of induction heating coil. Induction heating coil can be controlled by single phase sinusoidal pulse width modulation (SPWM) inverter .All electrical requirements beside magnetic permeability and resistivity variation for modeling induction heating coil have been considered to make simulations practical .Control ...

Fuzzy Modeling is an effective approach for System Identification. In its turn, Data Driven Fuzzy Modeling (DDFM) extracts these models from a set of input-output observations about the system. One way to carry out a DDFM process is by means of a combination of techniques, each one solving one of the DDFM phases. In this paper, we apply hybridizations of clustering algorithms and neural network...