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In recent years, the wireless sensors networks raise a great attention in the world. In this paper we proposed a method — multi-innovation coupled stochastic gradient (MICSG) algorithm for the global parameters estimation of the distributed sensors. This algorithm utilizes the identified result of the previous adjacent node and the local historical data to modify own estimated parameters. Then we...
This paper is concerned with preliminary results on robot vibratory modes on-line estimation. The dominating oscillatory mode of the robot arm is isolated by comparing the robot position given by the motors encoders and an external measure at the tool-tip of the robot arm. In this article the external measurement is provided by a laser tracker. The isolation of the oscillation permits to identify...
This paper addresses the problem of state and parameter estimation for the class of affine systems in the state space representation. The method does not require a specific state representation of the system and consists of designing a switched observer that, under certain conditions given in the paper, allows for the state and parameter estimation errors to converge to zero. Assuming that the parameters...
With reference to linear time invariant fractional-order systems, of both commensurate and non-commensurate type, a novel, gradient-based, procedure for the adaptive estimation of the delay parameter is presented in the current paper. The connections between the proposed delay estimation algorithm and a recently proposed technique for commensurate order estimation are highlighted and discussed. The...
A recursive least-squares (LS) state-space identification method based on the QR decomposition is proposed for non-uniformly sampled-data systems. Both cases of measuring all states and only the output(s) are considered for model identification. For the case of state measurement, a QR decomposition-based recursive LS (QRD-RLS) identification algorithm is given to estimate the state matrices. For the...
In prediction error method, it is known that the sequence of the criterion function converges uniformly in the parameter with probability one as the length of the input-output data tends to infinity. When the minimizing points of the limiting function criterion are not unique, the convergence of parameter estimation is not guaranteed in general. Two cases are distinguished. The case one is that the...
Presented in this paper is an extended version of the Multi-ADAptive LINear Element (MADALINE) neural network, termed EMADALINE, for On-line System identification of Multi-Input Multi-Output (MIMO) linear time-varying (LTV) systems Trained by Levenberg-Marquardt Method. A sliding window on the data set is used in the learning algorithm for the purpose of improving convergence speed during training...
This paper presents a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems, which can guarantee the prescribed error convergence performance (e.g. overshoot, convergence rate). By introducing appropriate filter operations, an explicit expression of parameter estimation error is obtained. Then a prescribed performance function (PPF) and the associate transform...
This paper considers the estimation of graphical model parameters with distributed data collection and computation. We first discuss the use and limitations of well-known distributed methods for marginal inference in the context of parameter estimation. We then describe an alternative framework for distributed parameter estimation based on maximizing marginal likelihoods. Each node independently estimates...
This paper studies recursive nonlinear least squares parameter estimation in inference networks with observations distributed across multiple agents and sensed sequentially over time. Conforming to a given inter-agent communication or interaction topology, distributed recursive estimators of the consensus + innovations type are presented in which at every observation sampling epoch the network agents...
Common to areas involving waves and fields like acoustics and electromagnetics is the need to evaluate frequency spectra and far-field radiation patterns. This has usually been done on a point-wise basis, sampling a first-principles or generating model (GM) finely enough to ensure that important features such as narrow resonances and nulls are not missed. This can lead to many more samples, and consequently...
An online approach to system identification based on the least-mean squares (LMS) algorithm is presented in this paper. This recursive method is actually an extended version of the LMS-like identification method based on binary observations (LIMBO), whose practical requirement is a simple comparator (1-bit quantizer). This method can be applied in the case of finite impulse response (FIR) systems...
In this paper, we re-visit the adaptive observer design of a class of uncertain nonlinear systems, where the nonlinearities before the unknown parameters are functions of unmeasured states. A nonlinear reduced-observer is developed by using Lyapunov techniques to estimate unmeasured states. The proposed observer design removes the global Lipschitz restriction. It is shown that the developed adaptive...
For linear time-invariant system model, this paper analyzes the convergence of parameter estimations as the length of the input-output data tends to infinity through prediction error method. It is known that the sequence of the prediction errors, called criterion functions, converges uniformly in the parameter with probability one as the data length tends to infinity. Given an input-output data of...
In this paper we investigate the problem of providing consistency, availability and durability for Web Service transactions. We consider enforcement of integrity constraints in a way that increases availability while guaranteeing the correctness specified in the constraint. We study hierarchical constraints that offer an opportunity for optimization because of an expensive aggregation calculation...
This paper is concerned with guaranteed parameter estimation in nonlinear dynamic systems in a context of bounded measurement error. The problem consists of finding—or approximating as closely as possible—the set of all possible parameter values such that the predicted outputs match the corresponding measurements within prescribed error bounds. An exhaustive search procedure is applied, whereby the...
This paper proposes a hybrid Newton-Raphson and genetic algorithm for the estimation of double cage induction motor parameters from commonly available manufacturer data. The hybrid algorithm was tested on a large data set of 6,380 IEC and NEMA motors and then compared with a baseline Newton-Raphson algorithm. The simulation results show that while the proposed hybrid algorithm is more computationally...
The convergence analysis of an online system identification method based on binary-quantized observations is presented in this paper. This recursive algorithm can be applied in the case of finite impulse response (FIR) systems and exhibits low computational complexity as well as low storage requirement. This method, whose practical requirement is a simple 1-bit quantizer, implies low power consumption...
This paper investigates the identification of the finite impulse response (FIR) systems with binary-valued observations. Combining with the stochastic gradient algorithm and statistical property of the system noise, a recursive projection algorithm is proposed to estimate the unknown parameters. Under some mild conditions on the a priori knowledge of the unknown parameters and inputs, the algorithm...
In this paper, we present a new current sharing technique on a general case of N paralleled DC-DC boost converters. The proposed optimization is based on the knowledge of individual boosts parameters. Every losses through the structure are modelled by equivalent resistors. Using an accurate online estimation of those resistors, we can determine the losses through each individual converter. Then, we...
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