In general, the first and the most important step in system analysis, prediction and control is the proper model of the system. In order to design the controller of nonlinear electrohydraulic system, several modeling techniques are proposed: the transfer function of the electrohydraulic system is identified using first-principle method, and the intelligent models are built by fuzzy modeling and...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

This paper proposes an approach to fuzzy modeling of magnetic levitation systems. These unstable and nonlinear processes are first linearized around several operating points, and next stabilized by a State Feedback Control System (SFCS) structure. Discrete-time Takagi-Sugeno (T-S) fuzzy models of the stabilized processes are derived on the basis of the modal equivalence principle, and the rule ...

Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an adaptive control scheme for a class of nonlinear systems. Firstly, the fuzzy T-S model is used to approximate the nonlinear systems, and the fuzzy control law of the fuzzy model is derived by the linear matrix inequality. Secondly, the adaptive fuzzy logic systems are con...

This paper briefly elaborates on a development in (applied) fuzzy logic that has taken place in the last couple of decades, namely, the complementation or even replacement of the traditional knowledge-based approach to fuzzy rule-based systems design by a data-driven one. It is argued that the classical rule-based modeling paradigm is actually more amenable to the knowledge-based approach, for ...

<span style="color: #000000; font-family: Tahoma, sans-serif; font-size: 13px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: justify; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; display: inline !important; float: none; backgro...

In this paper a hybrid learning system that combines different fuzzy modeling techniques is being investigated. In order to implement the different methods, we propose the use of intelligent agents, which collaborate by means of a multiagent architecture. This approach, involving agents which embody the different problem solving methods, is a potentially useful strategy for enhancing the power ...

In this research paper, ANFIS modeling and validation of Vestas 660 kW wind turbine based on actual data obtained from Eoun-Ebn-Ali wind farm in Tabriz, Iran, and FAST is performed. The turbine modeling is performed by deriving the non-linear dynamic equations of different subsystems. Then, the model parameters are identified to match the actual response. ANFIS is an artificial intelligent tech...

In this paper we focus on the problem of mammographic interpretation and in the construction of a computerized system to assess physicians in this task. We propose a solution based on fuzzy systems, and apply our fuzzy modeling methodology—Fuzzy CoCo—to find systems exhibiting both good performance and high interpretability. The results presented show the capacity of Fuzzy CoCo to find systems ...

We propose a model of a middleware system enabling personalized web search for users with different preferences. We integrate both inductive and deductive tasks to find user preferences and consequently best objects. The model is based on modeling preferences by fuzzy sets and fuzzy logic. We present the model-theoretic semantic for fuzzy description logic f-EL which is the motivation of creati...