The direct weight optimization (DWO) approach is a method for finding optimal function estimates via convex optimization, applicable to nonlinear system identification. In this paper, an extended version of the DWO approach is introduced. A general function class description — which includes several important special cases — is presented, and different examples are given. A general theorem abou...

In this work we develop an input-output recurrent neurofuzzy network in discretetime for identification and control of nonlinear systems. The structure is linear in the consequent parameters and nonlinear in the antecedent ones. The training of the antecedent parameters is achieved by linearizing them around a suboptimal value, assuming that the only known data are input-output signals obtained...

A model structure comprising a wavelet network and a linear term is proposed for nonlinear system identification. It is shown that under certain conditions wavelets are orthogonal to linear functions and, as a result, the two parts of the model can be identified separately. The linear-wavelet model is compared to a standard wavelet network using data from a simulated fermentation process. The r...

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properti...

background:  aeromonas spp. can cause diarrhea and various infections in humans. access to rapid techniques with a high sensitivity and specificity is strongly needed for the identification of aeromonas species. the aim of this study was to evaluate two different methods including api 20e bacterial identification tests and the molecular detection using pcr primers specific for 16s-rrna and 23s-...

We present a unifying view of discrete-time operator models used in the context of finite word length linear signal processing. Comparisons are made between the recently presented gamma operator model, and the delta and rho operator models for performing nonlinear system identification and prediction using neural networks. A new model based on an adaptive bilinear transformation which generaliz...

This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the differen...

Combining a note by Rissanen and an idea of enumerative coding we obtain a new implementation of the Ziv-Lempel incremental parsing algorithm for coding and decoding discrete data sequences. Index Terms -Ziv-Lempel algorithm, enumerative coding.

A new method called cuckoo search (CS) is used to extract and learn the Takagi-Sugeno (T-S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T-S fuzzy model. These parameters are learned simultaneously. The optimized T-S fuzzy model is validated by using t...