A weak nonlinear plant can be linearized and will track an input signal if the plant is preceded by a nonlinear controller which approximates the inverse of the plant’s transfer function. Present techniques for adjusting the controller adaptively to the plant require an additional nonlinear adaptive filter to perform a separate system identification. Straightforward update algorithms can not di...

Considered as a prototype of nonlinear system models the second order Volterra filter (FV2) is characterized by an increased complexity in comparison with a linear filter. This complexity is given by the large number of filter coefficients, as well as by the filter operations and it requires an increased computational power in technical applications. The filter based on the multi memory decompo...

The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed income applications. We investigate if the unscented Kalman filter should be used to capture nonlinearities, and compare the performance of the Kalman filter to that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in th...

Recently, particle filters have attracted attentions for nonlinear state estimation. In this approaches, a posterior probability distribution of the state variable is evaluated based on observations in simulation using so-called importance sampling. We proposed a new filter, Evolution Strategies based particle (ESP) filter to circumvent degeneracy phenomena in the importance weights, which dete...

Here we present a primal-dual interior point multidimensional filter line search method for nonlinear programming. The filter relies on a multidimensional feasibility measure enforcing every constraint violation to zero. To prevent convergence to feasible nonoptimal points a sufficient reduction criterion is also imposed on the barrier function. The evaluation of the method is until now made on...

This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Ga...

A nonlinear harmonic estimator (NHE) is proposed for extracting a harmonic signal and its fundamental frequency in the presence of white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a multiple sinusoidal model with staterepresentation and then efficiently specializing it for the case of harmonic estimation. The effectiveness of the NHE is verified usi...

unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

We present a new and systematic method of approximating exact nonlinear lters with nite dimensional lters, using the diierential geometric approach to statistics. We deene rigorously the projection lter in the case of exponential families. We propose a convenient exponential family, which allows to simplify the projection lter equation, and to deene an a posteriori measure of the performance of...

This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated c...