This report investigates how neurons with complex dynamics, specifically adaptation, can be incorporated into the Neural Engineering Framework. The focus of the report is fitting a linear-nonlinear system model to an adapting neuron model using system identification techniques. By characterizing the neuron dynamics in this way, we hope to gain a better understanding of what sort of temporal bas...

In this paper, a wavelet based method is proposed to identify the constant coefficients of a second order linear system and is compared with the least squares method. The proposed method shows improved accuracy of parameter estimation as compared to the least squares method. Additionally, it has the advantage of smaller data requirement and storage requirement as compared to the least squares m...

This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition based least squares iterative algorithm is presented for estimating the parameter vectors in each subsystem. Moreover, a data filtering based decomposition least squares iterative algorithm is proposed. The simulation...

The paper deals with a comparison of different optimization methods to identification of fractional order dynamical systems. The fractional models of the examples of physical systems ultracapacitors are established. Then different real frequency responses data from a laboratory setup of the processes are collected and the comparison of identification methods based on least squares and total lea...

This paper proposes an auxiliary model based hierarchical least squares algorithm for multivariable Box-Jenkins-like systems using the hierarchical identification principle. To improve the computational efficiency, a multivariable system is decomposed into two subsystems by using the data filtering technique. Furthermore, this paper presents a data filtering based auxiliary model hierarchical l...

This work focuses on friction and table mass identification using signal processing techniques. This report describes the system, data collection process and analysis of the data. Keywords—Friction Curve Identification, Table Mass Identification, Least Squares, Sampling Frequency, Butterworth Filter

Indexing terms : adaptive algorithm, system identification Abstract : In the presence of input interference, the Wiener solution for impulse response estimation is biased. In this Letter, it is proved that bias removal can be achieved by proper scaling the optimal filter coefficients and a modified least mean squares (LMS) algorithm is then developed for accurate system identification in noise....

One kind of the colored noise interference systems is generalized output error model (OEARMA). This paper presents a two-stage recursive least squares algorithm for OEARMA. Aiming at the OEARMA, this paper puts forward a two-stage recursive least squares algorithm. The basic idea of the algorithm is to combinie the auxiliary model identification idea and the decomposition technique to decompose...