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The present study proposes a new approach to identify the parameters of both continuous and discrete time Hammerstein systems in frequency domain. A harmonic probing technique is used to derive the linear and higher-order frequency response functions (called the generalized frequency response functions (GFRF)) of both discrete and continuous-time Hammerstein models. The computation of the n-th order...
In this work, an algorithm that identifies Hammerstein models with support vector machine nonlinearities and output-error linear dynamics is proposed. This algorithm is used to identify a Hammerstein model of stretch reflex EMG dynamics from experimental data.
Multiple channel blind system identification (MBSI) is often used in applications where the input signal cannot be measured and its statistical properties are unknown. Traditionally, the channel dynamics are modeled using finite impulse response filters. The number of model parameters can be significantly reduced if the filters are expanded onto a suitably chosen expansion basis, such as the discrete...
A growing emphasis on the analysis of time- varying systems has intensified the need for simpler and more efficient identification methods for these systems. In this contribution, we examine the time-varying Hammerstein structure, comprising a memoryless nonlinearity with time-varying parameters followed by a time-varying linear filter. Two existing approaches for the identification of these systems,...
The dynamic behavior of the human ankle during posture and movement can be described by joint stiffness, which is defined as the relation between the angular position of a joint and the torque acting about it. Joint stiffness can be separated into intrinsic stiffness and reflex stiffness, which are modeled as a linear system and a Hammerstein system, respectively. A two-pathway parallel cascade model,...
Tissue sensing adaptive radar (TSAR) is a microwave imaging technique that has been proposed as a modality for early stage breast cancer detection. A considerable challenge for the successful implementation of this technique is the reduction of clutter that is present in the scattered fields. In this paper, a method to estimate the tumor response contained in the late time scattered fields is presented...
A separable least squares algorithm is developed for the identification of a Wiener model whose dynamic element is a constant phase model that has been modified to include a purely viscous term. The separation of variables reduces the dimensionality of the search space from 5 to 2, greatly simplifying the optimization procedure used to estimate the parameters, The algorithm is tested on experimental...
Most physical systems are nonlinear and often time-varying. Constructing accurate models for nonlinear systems require specialized model structures that include their nonlinearities, whereas models of time-varying systems must include the time courses of the model's parameters. This contribution implements a technique in which the time dependence of the system's parameters are modeled by projecting...
The force and position data issued to construct models of joint dynamics are often obtained from closed-loop experiments, where the joint position is perturbed using an actuator configured as a position servo. If the position servo is orders of magnitude staffer than the joint, as is often the case, it is possible to treat the data as if they were obtained in open loop. It may be more relevant to...
System identification is a top-down approach that produces a mathematical model of a system from measurements of its inputs and outputs. These techniques can be used in a wide variety of fields including biomedical systems. Temporal basis expansion methods and ensemble techniques are two such specialized system identification techniques developed for identifying time-varying systems. In this paper,...
Discretization of dynamic stiffness models is shown to produce an a-causal impulse response. Expressions are derived for discrete-time impulse responses of first- and second-order derivatives. Enforcing periodicity gives rise to anti-causal components in these models. These models are used to describe the anti-causal components in identified stiffness models
System identification involves creating mathematical models of systems using measurements of their inputs and outputs. Linear time-varying systems form an important sub-class of models that require the use of specialized system identification techniques. One such approach involves expanding the time-varying parameters onto a set of temporal basis functions and then estimating the resulting expansion...
We have developed an algorithm for selecting an optimal set of inputs for use in linear multiple-input, single-output system identification processes. The algorithm provides a decomposition of the system output such that each component is uniquely attributable to a specific input This reduces the complexity of the estimation problem by optimally selecting inputs according to the uniqueness of their...
Nonlinear system identification techniques are often used to construct mathematical models of physiological systems. There is a wide variety of model structures, many of which require specialized techniques for their identification. Unfortunately, this variety may present a significant barrier to any non-specialists who wish to use these methods. This work describes a MATLAB/spl trade/ based toolbox...
Several nonparametric system identification techniques have been used to estimate the dynamic joint stiffness of the human elbow. Most studies involved a very stiff environment, but some studies have also shown that stiffness is modified in response to environmental compliance. However, using the same identification technique used under very stiff conditions to do identification under compliant conditions...
Vibroarthrographic signals have been proposed as a noninvasive tool for the diagnosis of joint injury. Models of VAG generation and transmission are required before application of this technique can begin. An experiment has been designed and performed to estimate sound transmission in the human knee at set joint angles. Linear frequency domain models and linear and nonlinear time domain models were...
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