Calculate Prediction Error Research Articles

Prediction of the wrist joint position during a postural tremor using neural oscillators and an adaptive controller

The prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. Thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter-burst intervals. Since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underlie the proposed model. Two neural oscillators were adopted. Electromyogram (EMG) signals were recorded from the extensor carpi radialis and flexor carpi radialis muscles concurrent with the joint angle signals of a stroke subject in an arm constant-posture. The output frequency of each oscillator was equal to the frequency corresponding to the maximum value of power spectrum related to the rhythmic wrist joint angle signals which had been recorded during a postural tremor. The phase shift between the outputs of the two oscillators was equal to the phase shift between the muscle activation of the wrist flexor and extensor muscles. The difference between the two oscillators' output signals was considered the main pattern. Along with a proportional compensator, an adaptive neural controller has adjusted the amplitude of the main pattern in such a way so as to minimize the wrist joint prediction error during a stroke patient's tremor burst and a healthy subject's generated artificial tremor. In regard to the range of wrist joint movement during the observed rhythmic motions, a calculated prediction error is deemed acceptable.

Arithmetic and local circuitry underlying dopamine prediction errors.

Dopamine neurons are thought to facilitate learning by comparing actual and expected reward1,2. Despite two decades of investigation, little is known about how this comparison is made. To determine how dopamine neurons calculate prediction error, we combined optogenetic manipulations with extracellular recordings in the ventral tegmental area (VTA) while mice engaged in classical conditioning. By manipulating the temporal expectation of reward, we demonstrate that dopamine neurons perform subtraction, a computation that is ideal for reinforcement learning but rarely observed in the brain. Furthermore, selectively exciting and inhibiting neighbouring GABA neurons in the VTA reveals that these neurons are a source of subtraction: they inhibit dopamine neurons when reward is expected, causally contributing to prediction error calculations. Finally, bilaterally stimulating VTA GABA neurons dramatically reduces anticipatory licking to conditioned odours, consistent with an important role for these neurons in reinforcement learning. Together, our results uncover the arithmetic and local circuitry underlying dopamine prediction errors.

Error Metrics and the Sequential Refinement of Kriging Metamodels

Metamodels, or surrogate models, have been proposed in the literature to reduce the resources (time/cost) invested in the design and optimization of engineering systems whose behavior is modeled using complex computer codes, in an area commonly known as simulation-based design optimization. Following the seminal paper of Sacks et al. (1989, “Design and Analysis of Computer Experiments,” Stat. Sci., 4(4), pp. 409–435), researchers have developed the field of design and analysis of computer experiments (DACE), focusing on different aspects of the problem such as experimental design, approximation methods, model fitting, model validation, and metamodeling-based optimization methods. Among these, model validation remains a key issue, as the reliability and trustworthiness of the results depend greatly on the quality of approximation of the metamodel. Typically, model validation involves calculating prediction errors of the metamodel using a data set different from the one used to build the model. Due to the high cost associated with computer experiments with simulation codes, validation approaches that do not require additional data points (samples) are preferable. However, it is documented that methods based on resampling, e.g., cross validation (CV), can exhibit oscillatory behavior during sequential/adaptive sampling and model refinement, thus making it difficult to quantify the approximation capabilities of the metamodels and/or to define rational stopping criteria for the metamodel refinement process. In this work, we present the results of a simulation experiment conducted to study the evolution of several error metrics during sequential model refinement, to estimate prediction errors, and to define proper stopping criteria without requiring additional samples beyond those used to build the metamodels. Our results show that it is possible to accurately estimate the predictive performance of Kriging metamodels without additional samples, and that leave-one-out CV errors perform poorly in this context. Based on our findings, we propose guidelines for choosing the sample size of computer experiments that use sequential/adaptive model refinement paradigm. We also propose a stopping criterion for sequential model refinement that does not require additional samples.

Residual life prediction from statistical features and a GARCH modeling approach for aircraft generators

Condition-based maintenance is currently widely used in the aviation industry with diagnoses obtained from the performance data of the aircraft. Online assessments of the real-time condition and predicted residual life have been of great importance for both mechanics and pilots, especially during flight for the latter. Statistical distribution and feature parameters are believed to be crucial criteria of performance degradation, which facilitate making practical component replacement decisions. Furthermore in terms of observations featuring performance degradation, time-series analysis provides feasible forecasts of residual life from the available working time of aero parameters. The recorded data from constant speed generator drives of aircraft generally demonstrate these characteristics, are non-stationary and have time-varying variance in time-series analysis. The generalized autoregressive conditional heteroskedasticity approach is appropriate to the situation to obtain prediction results. The suitability of the proposed method has been examined through calculating prediction errors with data from an actual life experiment of aviation generator.

RESERVOIR PREDICTING METHOD BASED ON SEISMIC ATTRIBUTE ANALYSES: A CASE STUDY OF ES2x IN YONG 3 BLOCK OF YONG’AN AREA

A new method is described for predicting reservoir properties using seismic data based on sedimentary micro-facies and sand thickness features of ES2x in Yong 3 block of Yong'an area.Conventional reservoir predicting method is to cross-plot the target data and seismic attribute for deriving the desired relationship between the two,and has a low predictive precision.Probabilistic neural network (PNN) method uses the convolutional operator to resolve the frequency difference between seismic attribute and the log data.The reliability of reservoir predicting results can be checked by cross-validation.Cross-validation divides the entire training data set into two subsets:the training data set and the validation data set.The training data set is used to derive the transform,while the validation data set is used to measure its final prediction error.Validation error can be used to check the validity of the attributes transform.On the basis of above research,6 optimum attributes susceptive to porosity are selected.Predicting precision is appraised by calculating prediction error and validation error and by correlating the prediction porosity and actual porosity.The reservoir porosity of sand layer of ES2x in Yong 3 block of Yong'an area is predicted successively by using PNN at last.

Construction of a five-marker protein-based model for stage-independent assessment of prognosis in breast cancer

11013 Background: While the TNM method for assessment of stage in breast cancer is simple and robust, molecular methods for limited patient subsets are gaining popularity (i.e Oncotype Dx or Mammaprint). We hypothesized that multiplexed quantitative measurement of proteins known to be involved in breast cancer signaling pathways of growth, proliferation, survival, and metastasis can enhance clinical methods of predicting prognosis in all patients. Methods: We assessed the expression of twenty-three proteins (ER, PR, EGFR, HER2, HER3, HER4, ERK, PTEN, PI3Kp85α, PI3Kp110α, p27/Kip1,EIF4E, FOXO3,AKT1, AKT2, AKT3, MYC, cyclinD1, FOXO1, mTOR, p70S6Kb, NFkB and BCL2) in four subcellular compartments by automated quantitative analysis of protein expression (AQUA) on tissue microarrays of the archival Yale breast cancer cohort (n=676). To project future performance of these markers including clinicopathological parameters, we constructed univariate and multivariate logistic regression models using leave-one-out cross-validation and calculated prediction error (PE) estimates of each model's value to predict a binary endpoint of 10 year survival. In addition, we constructed univariate and multivariate Cox models of ten year disease specific survival (DSS). Results: By Cox univariate analysis, ER, PR, PTEN, and BCL2 were directly correlated with DSS, while FOXO1, HER2, HER3, and PI3Kp110α were inversely correlated with DSS. A five-variable logistic regression model of 10 year survival including nuclear AKT1, BCL2, nuclear FOXO1, cytoplasmic mTOR, and nuclear p70S6Kb (prediction error= .274) surpasses performance of TNM staging (PE=.367) and the Nottingham Prognostic Index (PE=.326). The same model is associated with 10-year DSS by Cox proportional hazard (p=<.00001) independent of TNM stage and NPI. Conclusions: Our protein-based, multiplexed approach to prognostic classification was superior to traditional methods (TNM or NPI) and single biomarkers in this retrospective cohort. Current outcomes are influenced by modern therapies, limiting the direct impact of this analysis. However, molecular profiling of primary tumor linked to outcome paves the way for the incorporation of new prognostic models into prospective studies. [Table: see text]

Calculation of prediction error variances using sparse matrix methods

The use of exact and approximate algorithms to calculate prediction error variances using sparse matrix methods are demonstrated for an individual animal effect including maternal effects. One exact algorithm is substantially faster than two others. An approximation of the best exact method gave an acceptable level of reliabilities and reduced the computation by a factor of approximately fifty compared with the exact computation and is routine in national beef evaluation in Britain.

A random-field model-based algorithm for anomalous complex image pixel detection

Random-field model-based algorithms for the detection of anomalous pixels associated with complex valued imagery may be essential to robust focus of attention, target detection, and curing. The described algorithm includes the fitting of a specific class of causal, two-dimensional autoregressive random-field models to image data over specified estimation windows, and then subsequent construction of prediction error samples over specified detection windows. Statistical testing of the calculated prediction error samples is then used to localize anomalous image pixels. Experimental results obtained from running the described algorithm on SAR (synthetic aperture radar) imagery are included.

A note on the stock market effects of the adoption of risk-based capital requirements on international banks in different countries

The present note provides empirical evidence on the relative competitive effects of the adoption of risk-based capital requirements on large, international banks in the U.S., Canada, U.K., and Japan. A two-index regression model is used to calculate prediction errors in periods with numerous announcements concerning the new capital rules, including the Basle Conference. We find significant declines in equity returns for U.S., Canadian, and U.K. banks in response to news announcements, with U.S. bank stocks exhibiting the largest negative reaction. For Japanese banks, the equity return results are mixed, which may be due to uncertainty among investors regarding the handling of their sizeable hidden reserves under the new risk-adjusted capital rules.