New methods for the estimation of Takagi–Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T‐S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T‐S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T‐S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T‐S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T‐S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T‐S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use. Copyright © 2011 John Wiley & Sons, Ltd.     

Myology of the forelimb of Majungasaurus crenatissimus (Theropoda,Abelisauridae) and the morphological consequences of extreme limb reduction

Forelimb reduction occurred independently in multiple lineages of theropod dinosaurs. Although tyrannosaurs are renowned for their tiny, two‐fingered forelimbs, the degree of their reduction in length is surpassed by abelisaurids, which possess an unusual morphology distinct from that of other theropods. The forelimbs of abelisaurids are short but robust and exhibit numerous crests, tubercles, and scars that allow for inferences of muscle attachment sites. Phylogenetically based reconstructions of the musculature were used in combination with close examination of the osteology in the Malagasy abelisaurid Majungasaurus to create detailed muscle maps of the forelimbs, and patterns of the muscular and bony morphology were compared with those of extant tetrapods with reduced or vestigial limbs. The lever arms of muscles crossing the glenohumeral joint are shortened relative to the basal condition, reducing the torque of these muscles but increasing the excursion of the humerus. Fusion of the antebrachial muscles into a set of flexors and extensors is common in other tetrapods and occurred to some extent in Majungasaurus. However, the presence of tubercles on the antebrachial and manual elements of abelisaurids indicates that many of the individual distal muscles acting on the wrist and digits were retained. Majungasaurus shows some signs of the advanced stages of forelimb reduction preceding limb loss, while also exhibiting features suggesting that the forelimb was not completely functionless. The conformation of abelisaurid forelimb musculature was unique among theropods and further emphasizes the unusual morphology of the forelimbs in this clade.     

Deep temporal models and active inference

How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.     

Automated computation of the Gyrification Index in prefrontal lobes: methods and comparison with manual implementation

In this paper, we introduce an automated method of calculating Gyrification Index (GI), a measure of cortical folding. Automated GI (A-GI) is an in vivo GI implementation applied to MRI T1 weighted scans and is designed as an extension to the SPM analysis package. The A-GI tool is unbiased in its application, and is unlimited in the size of test cohort to which it can be applied. In comparison to manual methods, A-GI substantially reduces the time costs and improves repeatability. The current A-GI implementation is limited to analysis of prefrontal lobes, but an extension to provide whole brain A-GI is under consideration. In determination of the GI inner contour, A-GI traces high spatial frequencies typically missed in manual tracing, and thus, A-GI reports a high GI value. We examine the operation of this tool in two scan cohorts. We establish that the tool has good repeatability through its application to a cohort where 5 well individuals were scanned 5 times over a period of 6 months. This indicates that A-GI has low susceptibility to scanner noise and is not affected by the variability in brain representation given by repeat scans. We demonstrate replication of hand tracing results by comparisons with a manual GI study that has shown differences between high risk subjects who go on to develop schizophrenia and those who are at high risk but remain well. Direct scan by scan comparisons are carried out between manual and A-GI methods. In respect of scan orientation and coronal sampling, the methods differ, and these considerations contribute to a between methods right prefrontal ICC of 0.67 and left prefrontal ICC of 0.63. The replication results demonstrate that A-GI has discriminatory power equivalent to manual methods. A-GI is therefore a reliable measure of cortical folding that could be usefully applied to a number of MRI data sets of the brain in health and disease.     

Sources of Uncertainty in Intuitive Physics

Recent work suggests that people predict how objects interact in a manner consistent with Newtonian physics, but with additional uncertainty. However, the sources of uncertainty have not been examined. In this study, we measure perceptual noise in initial conditions and stochasticity in the physical model used to make predictions. Participants predicted the trajectory of a moving object through occluded motion and bounces, and we compared their behavior to an ideal observer model. We found that human judgments cannot be captured by simple heuristics and must incorporate noisy dynamics. Moreover, these judgments are biased consistently with a prior expectation on object destinations, suggesting that people use simple expectations about outcomes to compensate for uncertainty about their physical models.     

The effect of vitamin D on thyroid autoimmunity in euthyroid men with autoimmune thyroiditis and testosterone deficiency

BackgroundAutoimmune (Hashimoto’s thyroiditis) is characterized by a strong female preponderance, which may suggest that sex hormones have an impact on thyroid autoimmunity. The aim of this study was to investigate whether testosterone determines vitamin D action on thyroid antibody titers and thyroid function tests in men with autoimmune thyroiditis and low testosterone levels.MethodsThe study included 36 men with testosterone deficiency, 17 of whom had been treated for at least 26 weeks with oral testosterone undecanoate (120 mg daily). Because of coexistent euthyroid Hashimoto’s thyroiditis, all participants were then treated with vitamin D (100 μg daily). Serum titers of thyroid peroxidase and thyroglobulin antibodies, serum levels of thyrotropin, free thyroid hormones, testosterone and 25-hydroxyvitamin D, as well as Jostel’s thyrotropin index, SPINA-GT and SPINA-GD were assessed before vitamin D treatment and 26 weeks later.ResultsWith the exception of testosterone levels, there were no significant differences between both study groups in serum hormone levels, antibody titers and thyroid function tests. All participants completed the study. In addition to increasing 25-hydroxyvitamin D levels, vitamin D increased SPINA-GT and reduced thyroid peroxidase and thyroglobulin antibody titers. In testosterone-treated men, vitamin D increased testosterone levels. Vitamin D did not affect serum levels of thyrotropin, free thyroid hormones, Jostel’s thyrotropin index and SPINA-GD. Treatment-induced changes in thyroid antibody titers and SPINA-GT were more pronounced in testosterone-treated than testosterone-naïve men.ConclusionsThe obtained results suggest that the beneficial effect on thyroid autoimmunity and thyroid secretory function is stronger in men receiving testosterone therapy.     

Classification of samples into two or more ordered populations with application to a cancer trial

In many applications, especially in cancer treatment and diagnosis, investigators are interested in classifying patients into various diagnosis groups on the basis of molecular data such as gene expression or proteomic data. Often, some of the diagnosis groups are known to be related to higher or lower values of some of the predictors. The standard methods of classifying patients into various groups do not take into account the underlying order. This could potentially result in high misclasiffication rates, especially when the number of groups is larger than two. In this article, we develop classification procedures that exploit the underlying order among the mean values of the predictor variables and the diagnostic groups by using ideas from order‐restricted inference. We generalize the existing methodology on discrimination under restrictions and provide empirical evidence to demonstrate that the proposed methodology improves over the existing unrestricted methodology. The proposed methodology is applied to a bladder cancer data set where the researchers are interested in classifying patients into various groups. Copyright © 2012 John Wiley & Sons, Ltd.     

A Bayesian method for estimating prevalence in the presence of a hidden sub-population

When estimating the prevalence of a binary trait in a population, the presence of a hidden sub‐population that cannot be sampled will lead to nonidentifiability and potentially biased estimation. We propose a Bayesian model of trait prevalence for a weighted sample from the non‐hidden portion of the population, by modeling the relationship between prevalence and sampling probability. We studied the behavior of the posterior distribution on population prevalence, with the large‐sample limits of posterior distributions obtained in simple analytical forms that give intuitively expected properties. We performed MCMC simulations on finite samples to evaluate the effectiveness of statistical learning. We applied the model and the results to two illustrative datasets arising from weighted sampling. Our work confirms that sensible results can be obtained using Bayesian analysis, despite the nonidentifiability in this situation. Copyright © 2012 John Wiley & Sons, Ltd.