An analysis of the system identification methods have been carried out and a new alternative approach is proposed in order to estimate models for heave, pitch and roll dynamics of a high speed craft. As starting point, a first approach resolves the identification subject as an optimization problem to fit the best model, and uses genetic algorithms and nonlinear least squares with constraints me...

This paper investigates the use of a fuzzy method as a tool for model identification of a non linear and multivariable system when the measurement data is available. In fact, the use of fuzzy clustering facilitates automatic generation of Takagi-Sugeno rules and its antecedent parameters. After the determination of the consequent parameters, these are adapted by a recursive least squares algori...

The distribution of gluten and starch in wheat flour dough influences qualities of bread such as texture, and a method to visualize these distributions easily, accurately, and nondestructively would be of great value. The objective of this study was to develop an imaging method that visualizes the precise quantity of these two constituents, using the Fluorescence Fingerprint (FF). The FF is a s...

Least-squares method is the most popular method for parameter estimation. It is easy applicable, but it has considerable drawback. Under well-known conditions in the presence of noise, the LS method produces asymptotically biased and inconsistent estimates. One way to overcome this drawback is the implementation of the instrumental variable method. In this paper several modifications of this me...

In a companion paper [1], we studied amplitude estimation of one-dimensional (1-D) sinusoidal signals from measurements corrupted by possibly colored observation noise. We herein extend the results for twodimensional (2-D) amplitude estimation, which is of interest in various applications, including medical imaging, synthetic aperture radar (SAR), seismology, and many others. In particular, we ...

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...

In experimental science and engineering, least squares are ubiquitous in analysis and digital data processing applications. Minimizing sums of squares of some quantities can be interpreted in very different ways and confusion can arise in practice, especially concerning the optimality and reliability of the results. Interpretations of least squares in terms of norms and likelihoods need to be c...