An alternative solution to the model structure selection problem

An alternative solution to the model structure selection problem is introduced by conducting a forward search through the many possible candidate model terms initially and then performing an exhaustive all subset model selection on the resulting model. An example is included to demonstrate that this approach leads to dynamically valid nonlinear models     

Interface Interactions of Dissimilar Materials at Elevated Temperatures

This paper reviews some of the chemical interactions that occurred at the interface of ceramic/molten metal liquids. Control of interfacial reactions between dissimilar materials is an important issue in numerous technological applications, such as brazing of ceramics to metals, design of ceramic–metal composites, coatings of ceramics on metal substrates, and development of crucibles for melting of refractory metals. In ceramic/metal systems, wetting of the ceramic surface by the liquid metal is typically accompanied to some extent by interfacial reactions. The chemical incompatibility between the metal and non‐metallic materials can result in the formation of undesirable phases, due to the chemical and metallurgical reactions that take place during processing or in service. There is a need, therefore, to characterize the governing factors and reaction pathways at these interfaces. So, when the reaction products obtained during interdiffusion processing are not favorable, the diffusion pathway can be modified to control their formation.     

Accountability and the discussion of unshared, critical information in decision-making groups.

Previous research has demonstrated that groups are more likely to discuss information shared by all group members than unshared information (G. M. Wittenbaum & G. Stasser, 1996). In the present study, it was hypothesized that groups may be less likely to overlook unshared information when they are held accountable to an audience outside the group for their decisions. University students read a murder mystery and then met in 3-person groups to select who they thought was most likely to have committed the crime. Contrary to hypotheses, the results showed that accountable groups were less likely to focus on unshared information than groups who were not held accountable because of an increased focus on irrelevant details by accountable groups. Implications for future research are considered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Discrete time subharmonic modelling and analysis

Traditionally the Volterra time and frequency domain analysis tools cannot be applied to severely non-linear systems. In this paper, a new method of building a time-domain NARX MISO model for a class of severely SISO non-linear systems that exhibit subharmonics is introduced and it is shown how this allows the Volterra time and frequency domain analysis to be extended to this class of non-linear systems. The new approach is based on decomposing the original single input based on a Fourier analysis to provide a set of modified input signals which have the same period as the output signal. A MISO NARX model can then be constructed from the decomposed multiple inputs and the single output signal. The resulting MISO model is shown to meet the basic requirement for the existence of a Volterra series representation from which important frequency domain properties can be derived, explained and discussed. This is done by first introducing the derivation of generalized frequency response functions (GFRFs) from time domain MISO NARX models. The steady state response synthesis problem using the input spectrum and the MISO GFRFs is then investigated in order to verify the effectiveness and accuracy of the MISO modelling approach for severely non-linear systems. Finally a new frequency domain analysis method is introduced for systems that exhibit subharmonic oscillations.     

An adaptive orthogonal search algorithm for model subset selection and non-linear system identification

A new adaptive orthogonal search (AOS) algorithm is proposed for model subset selection and non-linear system identification. Model structure detection is a key step in any system identification problem. This consists of selecting significant model terms from a redundant dictionary of candidate model terms, and determining the model complexity (model length or model size). The final objective is to produce a parsimonious model that can well capture the inherent dynamics of the underlying system. In the new AOS algorithm, a modified generalized cross-validation criterion, called the adjustable prediction error sum of squares (APRESS), is introduced and incorporated into a forward orthogonal search procedure. The main advantage of the new AOS algorithm is that the mechanism is simple and the implementation is direct and easy, and more importantly it can produce efficient model subsets for most non-linear identification problems.     

Towards modelling complex robot training tasks through system identification

Previous research has shown that sensor–motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identification, where the sensory perception of the robot is mapped to the desired motor commands using non-linear polynomial functions, resulting in a tight coupling between sensing and acting — the robot responds directly to the sensor stimuli without having internal states or memory.However, competences such as for instance sequences of actions, where actions depend on each other, require memory and thus a representation of state. In these cases a simple direct link between sensory perception and the motor commands may not be enough to accomplish the desired tasks. The contribution of this paper to knowledge is to show how fundamental, simple NARMAX models of behaviour can be used in a bootstrapping process to generate complex behaviours that were so far beyond reach.We argue that as the complexity of the task increases, it is important to estimate the current state of the robot and integrate this information into the system identification process. To achieve this we propose a novel method which relates distinctive locations in the environment to the state of the robot, using an unsupervised clustering algorithm. Once we estimate the current state of the robot accurately, we combine the state information with the perception of the robot through a bootstrapping method to generate more complex robot tasks: We obtain a polynomial model which models the complex task as a function of predefined low level sensor–motor controllers and raw sensory data.The proposed method has been used to teach Scitos G5 mobile robots a number of complex tasks, such as advanced obstacle avoidance, or complex route learning.     

Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.     

Coping, stress, and social resources among adults with unipolar depression.

Used a stress and coping paradigm to guide the development of indices of coping responses and to explore the roles of stress, social resources, and coping among 424 men and women (mean age 40.7 yrs) entering treatment for depression. An expanded concept of multiple domains of life stress was used to develop several indices of ongoing life strains. A variety of measures were obtained from Ss, family members, and treatment staff. Two questionnaires completed by Ss included the Health and Daily Living Form and the Work Environment Scale. Although most prior studies have focused on acute life events, results of the present study show that chronic strains were somewhat more strongly and consistently related to the severity of dysfunction. The coping indices generally showed acceptable conceptual and psychometric characteristics and only moderate relationships to Ss' sociodemographic characteristics or to the severity of the stressful event for which coping was sampled. Coping responses directed toward problem solving and affective regulation were associated with less severe dysfunction, whereas emotional-discharge responses, more frequently used by women, were linked to greater dysfunction. Stressors, social resources, and coping were additively predictive of an S's functioning, but coping and social resources did not have stress-attenuation or buffering effects. (72 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented using a multivariate non-linear function, is initially decomposed into a number of functional components via the well-known analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-the-parameters problem, which can be solved using least-squares type methods. An efficient model structure determination approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to represent high-order and high dimensional non-linear systems.