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—This study presents a functional-link-based neuro-fuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent(More)
In this paper, a TSK-type quantum neural fuzzy network (TQNFN) for temperature control is proposed. The TQNFN model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the TQNFN model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of(More)
In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) for identification and prediction is proposed. The compensatory-based fuzzy method uses the adaptive fuzzy operations of neuro-fuzzy systems to make fuzzy logic systems more adaptive and effective. A recurrent network is embedded in the RCNFS by adding feedback connections in the second(More)
—This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables.(More)
Hyperhomocysteinemia (HHcy) is a risk factor for cognitive impairment. The purpose of this study was to determine the temporal pattern of cerebral pathology in a mouse model of mild HHcy, because understanding this time course provides the basis for understanding the mechanisms involved. C57Bl/6 mice with heterozygous deletion cystathionine β-synthase (cbs(More)
In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The proposed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi–Sugeno–Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of(More)
The proposed FLFNN controller uses functional link neural networks (FLNN) that can generate a nonlinear combination of the input variables as the consequent part of the fuzzy rules. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine(More)