Abstract. In this paper an algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamical systems is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) neuro-fuzzy structure from sam...

In this paper neuro-fuzzy technique is used for the first time in modeling eco-friendly furnace parameters to predict the melting rate of the molten metal required to produce homogenous and quality castings. The relationship between the process variables (input) viz. flame temperature, preheat air temperature, rotational speed of the furnace dome, percentage of excess air, melting time, fuel co...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...

Abstract: Recently, supervised artificial neural networks have obtained success to reveal and provide quantitative information concerning defects in TNDE (Thermographic NonDestructive Evaluation). Supervised neural networks may converge to local minimum and their training procedure are usually long. In this study, a neuro-fuzzy approach is applied to characterize subsurface defects in TNDE. Sim...

The composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two different local approaches: the neuro-fuzzy inference system and the lazy learning approach. A neuro-fuzzy system is an hybrid representation which combines the linguistic description of fuzzy infere...

The paper describes a Neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neurofuzzy network efficiently. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared ...

A neuro-fuzzy network approach is developed to model the nonlinear behavior of submicron metal-oxide semiconductor field-effect transistors (MOSFETs). The proposed model is trained and implemented as a MOSFET in a software environment. The training data are obtained through various simulations of a MOSFET Berkeley short channel insulated-gate field-effect transistor model 3 (BSIM3) in HSPICE, a...

the oxidative coupling of methane (ocm) performance over na-w-mn/sio2 at elevated pressures has been simulated by adaptive neuro fuzzy inference system (anfis) using reaction data gathered in an isothermal fixed bed microreactor. in the designed neuro fuzzy models, three important parameters such as methane to oxygen ratio, gas hourly space velocity (ghsv), and reaction temperature were conside...

in this article the methodology proposed by li and wang for mixed qualitative and quantitative modeling and simulation of temporal behavior of processing unit is reexamined and extended to more complex case. the main issue of their approach considers the multivariate statistics of principal component analysis (pca), along with clustered fuzzy digraphs and reasoning. the pca and fuzzy clustering...