Abstract: A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

Abstract: A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

Abstract: A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

Abstract: A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

Abstract: The present invention is a system and method for optimizing electrical circuits by means of derivative-free optimization. Tunable parameters such as component values, transistor sizes or model parameters are automatically adjusted to obtain an optimal circuit. Any method of measuring the performance of the circuit, including computer simulation, can be incorporated into the optimization technique, with no derivative requirements. An arbitrary continuous optimization problem can be posed, including an objective function, equality and inequality constraints, and simple bounds on the tunable parameters. The optimization technique is efficient and guarantees that it will find a locally optimal solution from any starting point. Further, the procedure includes a method of automatically recovering from electrical failure to enable automatic and productive circuit optimization. A set of measurement widgets is provided to automatically introduce the checking required to recover from electrical failure.

Abstract: The present invention is a system and method for optimizing electrical circuits by means of derivative-free optimization. Tunable parameters such as component values, transistor sizes or model parameters are automatically adjusted to obtain an optimal circuit. Any method of measuring the performance of the circuit, including computer simulation, can be incorporated into the optimization technique, with no derivative requirements. An arbitrary continuous optimization problem can be posed, including an objective function, equality and inequality constraints, and simple bounds on the tunable parameters. The optimization technique is efficient and guarantees that it will find a locally optimal solution from any starting point. Further, the procedure includes a method of automatically recovering from electrical failure to enable automatic and productive circuit optimization. A set of measurement widgets is provided to automatically introduce the checking required to recover from electrical failure.