Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g.

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g.

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g.

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g.

Abstract: A computer-implemented method for performing dynamic cost accounting for an enterprise, wherein the enterprise includes a costing system. The method includes programmatically retrieving input information for the costing system, e.g., from one or more (possibly remote) information sources over a network, dynamically updating the costing system in accordance with the retrieved input information to generate an updated costing system, and the updated costing system calculating one or more outputs which are usable in managing the enterprise. The retrieving and update may occur periodically, e.g., monthly, weekly, per hour, minute, second, millisecond, etc., or on demand. In some embodiments, the enterprise may further include one or more optimizers, wherein the optimizers are provided with the one or more outputs of the costing system, and executed to determine one or more optimal operating parameters for the enterprise.

Abstract: A system and method for optimizing transactions in an e-marketplace. An e-marketplace optimization server couples to a plurality of participant computers through a network, each of which is operated on behalf of a participant. The server hosts a site which provides the e-marketplace where goods and/or services are bought and sold among participants. The server also includes a transaction optimization program which mediates a transaction among the participants which best serves the needs of two or more of the participants. Each of the participant computers provides transaction information to the server, including constraints and/or objectives related to the transaction.