This paper considers the problem of using approximate methods for realizing the neural controllers for nonlinear multivariable systems. In [1] the NARMA-L1 and NARMA-L2 models were introduced as approximations of he NARMA model used for the representation of a SISO nonlinear dynamical systems. The advantage obtained from using NARMA-L1 and NARMA-L2 models is that control input u(k) occurs linea...

The objective herein is to demonstrate the feasibility of a real-time digital control of an inverted pendulum for modeling and control, with emphasis on nonlinear auto regressive moving average based neural network (NARMA). The plant of interest is a novel Gyroscopic Inverted Pendulum (GIP) device that is nonlinear and open-loop unstable. The GIP balances a pendulum on its free knife-edge base ...

Despite their theoretical limitations, ARIMA models are widely used in real-life forecasting tasks. Parzen has proposed an extension of ARIMA models: ARARMA models. ARARMA models consist of an AR model followed by an ARMA model. Following Parzen approach,-NARMA neural network are MLP, the units of which are simple non-linear ARMA-based models (-NARMA units). They are a non-linear extension of A...

This article presents a new connectionist architecture for stochastic univariate signal prediction. After a review of related statistical and connectionist models pointing out their advantages and limitations, we introduce the-NARMA model as the simplest non-linear extension of ARMA models. These models then provide the units of a MLP-like neural network: the-NARMA neural network. The associate...

We investigate dynamic versions of fuzzy logic systems (FLS’s) and, specifically, their non-Singleton generalizations (NSFLS’s), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonline...

The use of digital predistortion for linearizing a millimeter-wave power amplifier (PA) is investigated. A PA operating at 38 GHz is designed using an accurate non-quasi-static transistor model, taking into account both shortand long-term memory effects. A realistic test signal is then used for the identification of a nonlinear auto-regressive moving average (NARMA) behavioral model of the PA. ...

There have been many approaches to the use of neural networks in control systems, such as the NARMA-L2 Controller, introduced by Narendra and Mukhopadhyay. One disadvantage of this controller is that it often produces chattering in the control action. In this paper we investigate the addition of linear feedback to the NARMA-L2 controller in order to smooth the control action. We show that under...

Three main types of inflammatory Non-Allergic Rhinitis (NAR) have been defined: NAR infiltrated by eosinophils (NARES), by mast cells (NARMA), and by neutrophils (NARNE). A new particular type has been characterized with current infiltration by eosinophils and mast cells (NARESMA). The aim of this study is to evaluate the clinical and functional characteristics in patients with NARES, NARMA, NA...

The least squares (LS) can be used for nonlinear autoregressive (NAR) and nonlinear autoregressive moving average (NARMA) parameter estimation. However, for nonlinear cases, the LS results in biased parameter estimation due to its assumption that the independent variables are noise free. The total least squares (TLS) is another method that can used for nonlinear parameter estimation to increase...

• TOP OF ARTICLE • INPUT-OUTPUT SYSTEM REPRESENTATION • NONLINEAR DIFFERENTIAL ALGEBRAIC REPRESENTATION • VOLTERRA REPRESENTATION • STATE-SPACE REPRESENTATION • BILINEAR REPRESENTATION • NARMA REPRESENTATION • FUZZY-LOGIC NONLINEAR REPRESENTATION • NONLINEAR REPRESENTATION USING NEURAL NETWORKS • MODEL-FREE REPRESENTATION • FEATURES OF NONLINEAR REPRESENTATIONS • EXAMPLE • CONCLUDING REMARKS • ...