The aim of this study is to employ the novel Adaptive Network-based Fuzzy Inference System to optimize the torque applied on the knee link of a rehabilitation robot. Given the special conditions of stroke or spinal cord injury patients, devices with minimum error are required for performing the rehabilitation exercises. After examining the anthropometric data tables of human body, parameters su...

In this study, a novel method for identification and control of nonlinear systems is developed. The method proposed realizes the dynamics of a system by employing the Runge-Kutta method at the upper level. The intermediate level of the strategy constructs the architecture utilizing an adaptive neuro fuzzy inference system. The overall system is able to imitate the behavior of a complex dynamic ...

Background and Objectives: One of the problems in juice membrane clarification is the accumulation and deposition of rejected compounds on membrane surfaces or inside its pores which results in a membrane fouling. Materials and Methods: Several parameters can have influence on fouling in one hand and prediction of juice permeates flux during the membrane processing is of importance in indust...

Three Neuro-Fuzzy Inference Systems (ANFIS) including Grid Partitioning (GP), Subtractive Clustering (SCM) and Fuzzy C-means clustering Methods (FCM) have been used to predict the groutability of granular soil samples with cement-based grouts. Laboratory data from related available in litterature was used for the tests. Several parameters were taken into account in the proposed models: water:ce...

Finding the compromise between computational complexity and adaptation potential of linguistic fuzzy systems is important in several fields of application of fuzzy systems including fuzzy modeling and control. This paper considers the role of popular sand t-norms in fuzzy inference function in this aspect and presents some recently acquired results. First, it is shown that with simultaneous app...

Exact small-sample methods for discrete data use probability distributions that do not depend on unknown parameters. However, they are conservative inferentially: the actual error probabilities for tests and confidence intervals are bounded above by the nominal level. This article surveys ways of reducing or even eliminating the conservatism. Fuzzy inference is a recent innovation that enables ...

The survey and modeling of the deformations of large structures is a major task in engineering geodesy. In this paper, a new procedure to describe and predict the deformations is presented and discussed which is based on Neuro-Fuzzy modeling. Neuro-Fuzzy methods are data driven; they deduce the model directly from the data. Hence, they are mostly convenient if there are no physical models avail...

Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm converges and the tuning of fuzzy infer...

In this paper, a Fuzzy Prolog is introduced that is based on the fuzzy resolution principle. There are three levels of concern: (1) the f u z ~ truth values of predicates that depend on several values of individual variables, (2) the weights of rules that depend on several fuzzy truth values of premise and conclusion, and (3) the confidence of resolvent that depends on the fuzzy resolution prin...

Two-string fuzzy inference consists of two separate inference mechanisms: One conventional fuzzy inference system that processes recommending rules, as well as a mechanism for processing negative rules, which prevent the system from outputting their associated values when their premise is fulfilled. Two-string inference has valuable applications in pattern recognition and control tasks. We pres...