Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhancedmodel structures of electric motors that accommodate both normal and faulty mode...

Support Vector Machines (SVMs) have become a popular classification tool. Because of their theoretical robustness they offer improvements in pattern classification applications. This paper describes an approach of producing a N-best list of hypotheses for the needs of phoneme recognition, using a Least Squares Support Vector Machine classifier (LS-SVM) and generate the corresponding N-best list...

The work is concerned with identification of bad signatures in a sequence which is validated using batching. Identification codes (id-codes) are defined and their general properties are investigated. A taxonomy of idcodes is given. The generic construction for a wide range of id-codes is given and its instantiation using mutually orthogonal Latin squares is described. Hierarchical identificatio...

An iterative least squares algorithm and a recursive least squares algorithms are developed for estimating the parameters of moving average systems. The key is use the least squares principle and to replace the unmeasurable noise terms in the information vector. The steps and flowcharts of computing the parameter estimates are given. The simulation results validate that the proposed algorithms ...

Accurate multidimensional ground reaction forces and moments (GRF/Ms) can be predicted from marker-based motion capture using Partial Least Squares (PLS) supervised learning. In this study, the correlations between known and predicted GRF/Ms are compared depending on whether the PLS model is trained using the discrete inputs of sex, height and mass. All three variables were found to be accounte...

A new forward regression model identification algorithm is introduced. The derived model parameters, in each forward regression step, are initially estimated via orthogonal least squares (OLS) (using the modified Gram-Schmidt procedure), followed by being tuned with a new gradient descent learning algorithm based on the basis pursuit that minimizes the norm of the parameter estimate vector. The...

It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by noise. One possible approach for solving this bias problem is the bias-compensation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compnesation based method is proposed for identification of noisy input-output ...