We present an analytical technique that uses fault injection data for estimating the coverage of concurrent error detection mechanisms in microprocessors. A major problem in such estimations is that the coverage depends on the program executed by the microprocessor as well as the input sequence to the program. We propose a method that predicts the error coverage for a specified input sequence b...

A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space model is realized from a continuous-discrete-time linear stochastic system specified using transfer f...

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction er...

We outline a method for quantifying the error of a sequence prediction. With sequence predictions represented by semimeasures ν(x) we define their error to be − log2 ν(x). We note that enumerable semimeasures are those which model the sequence as the output of a computable system given unknown input. Using this we define the simulation complexity of a computable system C relative to another U g...