Appraisal-emotion relationships in daily life.

Using a daily process design, the present study examined relationships between momentary appraisals and emotional experience based on Smith and Lazarus' (1993) theory of emotions (1993). Nine times a day for 2 weeks, participants (N = 33, 23 women) recorded their momentary experience of 2 positive emotions (joy, love) and 4 negative emotions (anger, guilt, fear, sadness) and the core relational theme appraisal contents Smith and Lazarus hypothesized as corresponding to these emotions. A series of multilevel modeling analyses found that the hypothesized relationships between appraisal contents and these emotions were stronger than relationships between contents and other emotions, although appraisals were related to other emotions in many cases. Moreover, there were some individual differences in the strength of these relationships. These results suggest that there are no one-to-one relationships between appraisal contents and specific emotional experiences, and that specific emotions are associated with different appraisal contents, and that specific appraisals are associated with different emotions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Predicting the duration of emotional experience: Two experience sampling studies.

The authors present 2 studies to explain the variability in the duration of emotional experience. Participants were asked to report the duration of their fear, anger, joy, gratitude, and sadness episodes on a daily basis. Information was further collected with regard to potential predictor variables at 3 levels: trait predictors, episode predictors, and moment predictors. Discrete-time survival analyses revealed that, for all 5 emotions under study, the higher the importance of the emotion-eliciting situation and the higher the intensity of the emotion at onset, the longer the emotional experience lasts. Moreover, a reappearance, either physically or merely mentally, of the eliciting stimulus during the emotional episode extended the duration of the emotional experience as well. These findings display interesting links with predictions within N. H. Frijda's theory of emotion, with the phenomenon of reinstatement (as studied within the domain of learning psychology), and with the literature on rumination. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A two-mode clustering method to capture the nature of the dominant interaction pattern in large profile data matrices.

Profile data abound in a broad range of research settings. Often it is of considerable theoretical importance to address specific structural questions with regard to the major pattern as included in such data. A key challenge in this regard pertains to identifying which type of interaction (double ordinal, mixed ordinal/disordinal, double disordinal) most adequately fits the major pattern in a profile data set at hand. In the present article a novel methodology is proposed to deal with this challenge. This methodology is based on constrained and unconstrained versions of a recently introduced 2-mode clustering model, the real-valued hierarchical classes model. The methodology is illustrated using empirical Person × Situation profile data on altruism. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Self‐tuning control for polynomial systems using a receding horizon observer and control strategy

A receding horizon observer and control scheme is introduced for non‐linear systems described by polynomial maps. This control scheme has a natural interpretation as a two‐stage adaptive or self‐tuning control algorithm. The non‐linear feedback that results is defined only on the basis of past input and output measurements. The computational complexity aspects of this approach to adaptive or self‐tuning control are briefly discussed. A linear system and a Hénon map example are used to illustrate the ideas. Copyright © 2004 John Wiley & Sons, Ltd.     

Individual differences in situation–behavior profiles: A triple typology model.

A model is proposed to represent individual differences in situation–behavior profiles. The model consists of 3 components: (a) Typologies of person, situation, and behavior classes; (b) hierarchical relations between the classes of each typology; and (c) a characterization of the person types in terms of different sets of if (situation class) then (behavior class) rules by which the 3 typologies are linked to one another. A data analysis technique (INDCLAS) is available to induce a triple typology model from empirical data. To reveal the psychological mechanisms behind such a model, the classes of the model can be related to situation, behavior, and person features. As a result, person types can be interpreted in terms of systems of cognitive–affective variables that mediate between active situation features and behavioral manifestations. This is illustrated with a study on self-reported hostile behavior in frustrating situations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Three-mode partitioning

The three-mode partitioning model is a clustering model for three-way three-mode data sets that implies a simultaneous partitioning of all three modes involved in the data. In the associated data analysis, a data array is approximated by a model array that can be represented by a three-mode partitioning model of a prespecified rank, minimizing a least squares loss function in terms of differences between data and model. Algorithms have been proposed for this minimization, but their performance is not yet clear. A framework for alternating least-squares methods is described in order to offset the performance problem. Furthermore, a number of both existing and novel algorithms are discussed within this framework. An extensive simulation study is reported in which these algorithms are evaluated and compared according to sensitivity to local optima. The recovery of the truth underlying the data is investigated in order to assess the optimal estimates. The ordering of the algorithms with respect to performance in finding the optimal solution appears to change as compared to the results obtained from the simulation study when a collection of four empirical data sets have been used. This finding is attributed to violations of the implicit stochastic model underlying both the least-squares loss function and the simulation study. Support for the latter attribution is found in a second simulation study.     

A new approach to adaptive robust control

A new approach is given for the design of adaptive robust control in the frequency domain. Starting with an initial model of a stable plant and a robust stabilizing controller, the new (windsurfer) approach allows the bandwidth of the closed-loop system to be increased progressively through an iterative control-relevant system identification and control design procedure. The method deals with both undermodelling and measurement noise issues. Encouraging results are obtained in the simulations that illustrate the new idea.     

A dual purpose principal and minor component flow

Principal component flows are flows converging to the eigenvectors associated with the largest eigenvalues of a given symmetric matrix. Similarly, minor component flows converge to the eigenvectors associated with the smallest eigenvalues. Traditional flows required the matrix to be positive definite, and moreover, finding well-behaved minor component flows appeared to be harder and unrelated to the principal component case. This paper derives a flow which can be used to extract either the principal or the minor components and which does not require the matrix to be positive definite. The flow is shown to be a generalisation of the Oja–Brockett flow.     

A rigorous solution of the infinite time interval LQ problem with constant state tracking

A linear quadratic constant state tracking problem is considered over an infinite time horizon. It is shown that the solution cannot be obtained as a limit from a finite time horizon problem, as in general the limiting problem is ill-posed. To obtain a rigorous solution, the problem is split in two natural well-posed subproblems. One optimal control problem addresses the transient and the other optimal control problem concerns the steady state behavior. It is shown that the transient problem and the steady state problem are solved by the same control law.