Abstract: A user utilizing a user interface inputs debt information, income information and savings information. The user also inputs retirement information which includes aspects of the user's retirement plans (e.g., age of retirement, current age, etc.). The debt information, the income information, and the savings information are assigned to categories. The categories are prioritized to generate a savings plan that includes a savings prioritization (e.g., save into these types of accounts in this order) and/or a spending prioritization (e.g., spend from these types of accounts in this order). The savings plan and/or a quantified benefit of its use is displayed to the user.

Abstract: A device receives, from a customer, a request for an end-to-end path through a network, determines parameters of a query based on the request and path criteria, and executes the query on a database of network elements capable of being included in the end-to-end path. The device also selects one or more of the network elements provided in the database based on results of the query, and reserves, in the database, the one or more selected network elements for the end-to-end path.

Abstract: A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.

Abstract: A method of managing a process model history having process models stored therein, includes organizing the process models according to first and second priority criteria, wherein each process model is represented according to a combination of a value in connection with the first and second priority criteria. The representation may be coordinate values in a multi-dimensional space having dimensions corresponding to the first and second priority criteria. A degree of separation or relationship to a common point of reference is calculated for each process model, where the point of reference is a value in connection with the first and second priority criteria. A process model may be removed or selected for deletion based on the degree of separation or proximity in relation to the point of reference, subject to the total number of process models identified for the same control routine, and the total number of process models identified for the same operational region.

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained.

Abstract: A process controller that may be used to control a process having a set of process outputs effected by a set of process control input signals includes a multiple-input/multiple output controller that uses the process outputs to develop the set of process control input signals and a process model, which may be a non-linear process model, that receives the set of process control input signals to produce a prediction signal for one or more of the process outputs. The multiple-input/multiple-output control element includes another process model, which may be a standard linear process model, to develop a prediction vector for each of the process outputs and includes a correction unit that modifies the prediction vector for the one or more of the process outputs using the prediction signal for the one or more of the process outputs to thereby compensate for the non-linearities of the process.

Abstract: A method and apparatus that generates an estimate of a property of a batch process uses a non-parametric model to generate a plurality of rate of reaction estimates associated with the batch process. Each rate of reaction estimate may correspond, for example, to a particular time during the batch process. The plurality of rate of reaction estimates are then integrated to generate an estimate of a property of the batch at the particular time.

Abstract: A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Abstract: A process control configuration system is provided for use in creating or viewing an integrated optimization and control block that implements an optimization routine and a multiple-input/multiple-output control routine. The configuration system may enable a user to display or configure the optimizer or the control routine. A storage routine may store information pertaining to a plurality of control and auxiliary variables and to a plurality of manipulated variables to be used by the optimization routine and/or the control routine. A display routine may present a display to a user regarding the information pertaining to the plurality of control and auxiliary variables and to the plurality of manipulated variables.

Abstract: A method for inserting a prefix, including traversing a trie node block structure to obtain a trie node block in which to insert the prefix, determining whether the trie node block is associated with a hash table, if the trie node block is not associated with a hash table: calculating a set of hash values for a trie node in the trie node block, and populating the hash table using the set of hash values calculated for the trie node, and inserting the prefix in an appropriate location in the hash table using at least one of the set of hash values associated with the trie node.

Abstract: A process controller that may be used to control a process having a set of process outputs effected by a set of process control input signals includes a multiple-input/multiple output controller that uses the process outputs to develop the set of process control input signals and a process model, which may be a non-linear process model, that receives the set of process control input signals to produce a prediction signal for one or more of the process outputs. The multiple-input/multiple-output control element includes another process model, which may be a standard linear process model, to develop a prediction vector for each of the process outputs and includes a correction unit that modifies the prediction vector for the one or more of the process outputs using the prediction signal for the one or more of the process outputs to thereby compensate for the non-linearities of the process.

Abstract: A method and apparatus that generates an estimate of a property of a batch process uses a non-parametric model to generate a plurality of rate of reaction estimates associated with the batch process. Each rate of reaction estimate may correspond, for example, to a particular time during the batch process. The plurality of rate of reaction estimates are then integrated to generate an estimate of a property of the batch at the particular time.

Abstract: An adaptive predictive model includes a standard predictive model, such as a neural network or a natural model, constructed to produce an output that predicts a process parameter and a combiner network that combines the output of the predictive model with one or more measured values of the process parameter to produce an adjusted predicted process parameter during operation of a process. The adaptive predictive model reduces or corrects for non-linear as well as linear errors in the prediction of a process variable without having to reform the predictive model itself, while requiring only minor increases in processing power and time.

Abstract: A process control configuration system is provided for use in creating or viewing an integrated optimization and control block that implements an optimization routine and a multiple-input/multiple-output control routine. The configuration system may enable a user to display or configure the optimizer or the control routine. A storage routine may store information pertaining to a plurality of control and auxiliary variables and to a plurality of manipulated variables to be used by the optimization routine and/or the control routine. A display routine may present a display to a user regarding the information pertaining to the plurality of control and auxiliary variables and to the plurality of manipulated variables.