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The Fuzzy-neural development system is presented in two versions. The First version is the implementation in C, C++ with interface to Matlab or other programs using DLL calling. The second implementation is newer, conform to Referential transparency. For implementation we choose functional programing language Scheme. The Scheme version R5RS is adopted on different platforms like PC, microprocessors...
Considering the mathematical model for the control system of intermediate bending roll in UC rolling mill is time-varying and uncertain, the conventional PID algorithm can't achieve a rapid and accurate response when some parameters change, so that the precision of the sheet shape can't be easily ensured. To realize the accurate control for the intermediate bending roll, a fuzzy neural network controller...
This paper investigates a new technique for estimating the shape parameter of a K-distribution based on fuzzy neural network (FNN). In order to improve the estimation accuracy with inexpensive computational requirement, the FNN estimator is used to accurate the solutions of the nonlinear equations and the inverse functions (gk(nucirc))of the Raghavanpsilas and ML/MOM (Maximum-Likelihood and Method...
This paper addresses a novel approach based on neuro-fuzzy inference system to solve the estimation problem of the K-distributed parameters. The method is based on a network implementation with real weights and the real genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. The proposed FNN estimator is based on the arithmetic and...
In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system intelligence modeling was presented. In the processor, wavelet-fuzzy technique and neural network technique are combined. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function...
This work provides an effective approach based on adaptive neuro-fuzzy inference system to the solution of constant false alarm rate (CFAR) detection for Weibull clutter statistics. The optimal detection thresholds of the ML-CFAR (maximum-likelihood CFAR) detector in Weibull clutter with unknown shape parameter are obtained using fuzzy-neural networks (FNN) technique. The genetic learning algorithm...
This paper provides a novel approach based on neuro-fuzzy inference system for the estimation problem of the K-distributed parameters. The proposed method is based on a network implementation with real weights and the genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. Moreover, the proposed estimator combines the Raghavan's...
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