An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate Bspline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss–Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches

Elastic net prefiltering for two class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model’s generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems

Polymer nanocomposites for high-temperature composite repair

A novel repair agent for resin-injection repair of advanced high temperature composites was developed and characterized. The repair agent was based on bisphenol E cyanate ester (BECy) and reinforced with alumina nanoparticles. To ensure good dispersion and compatibility with the BECy matrix in nanocomposites, the alumina nanoparticles were functionalized with silanes. The BECy nanocomposites, containing bare and functionalized alumina nanoparticles, were prepared and evaluated for their thermal, mechanical, rheological, and viscoelastic properties. The monomer of BECy has an extremely low viscosity at ambient temperature, which is good for processability. The cured BECy polymer is a highly cross-linked network with excellent thermal mechanical properties, with a high glass transition temperature (Tg) of 270 yC and decomposition temperature above 350 yC. The incorporation of alumina nanoparticles enhances the mechanical and rheological properties of the BECy nanocomposites. Additionally, the alumina nanoparticles are shown to catalyze the cure of BECy. Characterization of the nanocomposites included dynamic mechanical analysis, differential scanning calorimetry, thermogravimetric analysis, rheological and rheokinetic evaluation, and transmission electron microscopy. The experimental results show that the BECy nanocomposite is a good candidate as repair agent for resin-injection repair applications

Modelling and inverting complex-valued Wiener systems

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memor

Analysis of movable rider/all-terrain vehicle system dynamics

Over the past decade, all-terrain-vehicles (ATVs) became a popular form of off-road recreational riding. Unfortunately, a large number of ATV accidents occurred causing incapacitating injuries and deaths to riders, many of whom were children under the age of 16. Although statistical data show that these vehicles are extremely dangerous, little engineering information is available to explain how these ATV accidents occur or why the accident rate is so high. Thus, a need exists to know more about the dynamic behavioral characteristics of ATVs so that the public understands more fully the ATVs\u27 lethal dangers and so that the designs of future ATVs are safer;The purpose of this research is to develop a more thorough understanding of the dynamic behavioral characteristics of ATVs. To accomplish this, several new mathematical models of ATV systems are developed. The new models allow for (1) the ATV to have either three or four wheels, (2) the rider to move relative to the ATV, and (3) the ATV system to be operated over a general three-dimensional ground profile. These models are used to determine some of the similarities and differences between the three- and four-wheeled ATVs and the effects that a movable rider can have on the handling and stability of ATVs when operated over various ground profiles. The computer simulation results of these models are compared with each other and to the results of a benchmark rigid rider ATV model;The development and simulation of these new ATV models provides a mechanism for studying the operational characteristics of ATV systems. The results obtained provide a better understanding on how these vehicles should be designed to improve their overturning stability and what riders should do to control these vehicles in avoidance maneuvers. This information will be useful in efforts to reduce the number of serious accidents which occur