In an intelligent manufacturing context, the smooth operations of mechanical equipment in production process enterprises and timely fault diagnosis during have become increasingly important. However, effect traditional depends on feature extraction quality experts’ empirical knowledge, which is inefficient costly, cannot match needs manufacturing. The TSK fuzzy system has a strong approximation...

To test the Turkish version of The Self-Efficacy for Home Exercise Programs Scale (SEHEPS-T) in patients with musculoskeletal diseases validity and reliability. performance scale was evaluated 122 varying diseases, repeated to assess its test-retest questionnaire applied included a Demographic Socioeconomic Characteristics Form, SEHEPS-T, (EXSE), Tampa Kinesiophobia (TSK). Exploratory Factor An...

Abstrad A Fuzzijed TSK-type Recurrent Neiiral Fuzzy Network (FTRNFN) for handling furry temporal information is proposed in this paper. The inputs and oulputs of FTRNFN are fuzzy pattems represented by Gaussian or isosceles triangular membership functions. In structure, FTRNFN is a recurrent fizzy nefwork constructed from a series of recurrent fuzzy +then rules with TSK-t)pe Consequent parts. T...

In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. Three different multi-objective evolutionary algorithms (MONEA, ENORA and NSGA-II) are used together with neural network techniques. These algorithms are checked out in the approximation of a dynamic non...

This paper presents aspects concerning the implementation of an adaptive Gravitational Search Algorithm (GSA) in the optimal tuning of fuzzy controllers. Takagi-Sugeno-Kang proportionalintegral fuzzy controllers (TSK PI-FCs) are designed and tuned for a class of nonlinear servo systems. The adaptive GSA is based on five stages to solve the optimization problems with objective functions which de...

This paper presents an adaptive stable generalized predictive control using Takagi-Sugeno-Kang (TSK) fuzzy model for nonlinear discrete-time with time-delay systems. This controller is composed of a fuzzy second-order time-delay identifier with on-line parameter estimation, and a stable generalized predictive control with abilities of accurate tracking and disturbance rejection. Numerical simul...

We propose a fuzzy-neural modeling approach for automatically constructing a fuzzy-neural model from a set of input-output data. The proposed approach consists of two phases, structure identification and parameter identification. In the structure identification phase, rough TSK fuzzy rules are extracted through a clustering algorithm. Then a fuzzy neural network is built in the parameter identi...

We describe an on-line dual detection method using HPLC for lipoprotein analysis that allows simultaneous determination of cholesterol and triglyceride profiles from a single injection of sample. Two different gel permeation columns, TSKgel LipopropakXL and Superose 6HR, were applied to the dual detection system, evaluating analytical performance of the proposed method and the columns by analyz...

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control...