Pavilion Technologies, Inc.

Penson Worldwide, Inc.

Ralph Bruce Ferguson

James David Keeler

Eric J Hartman

Kadir Liano

John P Havener

Devendra B Godbole

Liam Cheung

Ronald Scott Boyd

William Douglas Johnson

Bijan Sayyar-Rodsari

Carl Anthony Schweiger

Celso Axelrud

Edward Stanley Plumer

Eric Hurley

A method for modeling an investment significant parameter of a financial instrument, using a computer. At least one series of historical bid prices of the financial instrument or historical ask prices of the financial instrument is provided. A financial model type that has at least one variable parameter is selected. The variable parameter(s) of the selected financial model type is initialized. The series of historical bid prices and/or historical ask prices is applied to the initialized financial model type to estimate the variable parameter(s). The resulting model of the financial instrument may be used to predict future values of the investment significant parameter of the financial instrument. These predicted future values may be used to determine whether to perform automated trades of the financial instrument.

Modeling financial instruments using bid and ask prices

A plant is operable to receive control inputs c(t) and provide an output y(t). The plant () has associated therewith state variables s(t) that are not variable. A control network () models the plant by providing a predicted output which is combined with a desired output to generate an error that is back propagated through an inverse control network to generate a control error signal that is input to a distributed control system to vary the control inputs to the plant in order to change the output y(t) to meet the desired output. The inverse model represents the dependencies of the plant output on the control variables parameterized by external influences to the plant.

Residual activation neural network

A neural network system is provided that models the system in a system model () with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (). Input data is processed in a data preprocess step () to reconcile the data for input to the system model (). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor () which is utilized to control the output control (). The output control () is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model () exceeds a predetermined decision threshold, input by a decision threshold block (). Additionally, a validity model () is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (). This predicts the confidence in the predicted output which is also input to the decision processor (). The decision processor () therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block () can be utilized to train the system model ().

Method and apparatus for operating a neural network with missing and/or incomplete data

A system and method for historical database training of a support vector machine (SVM). The SVM is trained with training sets from a stream of process data. The system detects availability of new training data, and constructs a training set from the corresponding input data. Over time, many training sets are presented to the SVM. When multiple presentations are needed to effectively train the SVM, a buffer of training sets is filled and updated as new training data becomes available. Once the buffer is full, a new training set bumps the oldest training set from the buffer. The training sets are presented one or more times each time a new training set is constructed. A historical database of time-stamped data may be used to construct training sets for the SVM. The SVM may be trained retrospectively by searching the historical database and constructing training sets based on the time-stamped data.

System and method for historical database training of support vector machines

A system and method for performing modeling, prediction, optimization, and control, including an enterprise wide framework for constructing modeling, optimization, and control solutions. The framework includes a plurality of base classes that may be used to create primitive software objects. These objects may then be combined to create optimization and/or control solutions. The distributed event-driven component architecture allows much greater flexibility and power in creating, deploying, and modifying modeling, optimization and control solutions. The system also includes various techniques for performing improved modeling, optimization, and control, as well as improved scheduling and control. For example, the system may include a combination of batch and continuous processing frameworks, and a unified hybrid modeling framework which allows encapsulation and composition of different model types, such as first principles models and empirical models. The system further includes an integrated process scheduling solution referred to as process coordinator that seamlessly incorporates the capabilities of advanced control and execution into a real time event triggered optimal scheduling solution.

System and method for enterprise modeling, optimization and control

A neural network system is provided that models the system in a system model ( ) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control ( ). Input data is processed in a data preprocess step ( ) to reconcile the data for input to the system model ( ). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor ( ) which is utilized to control the output control ( ). The output control ( ) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model ( ) exceeds a predetermined decision threshold, input by a decision threshold block ( ). Additionally, a validity model ( ) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model ( ). This predicts the confidence in the predicted output which is also input to the decision processor ( ). The decision processor ( ) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block ( ) can be utilized to train the system model ( ).

A plant ( ) is operable to receive control inputs c(t) and provide an output y(t). The plant ( ) has associated therewith state variables s(t) that are not variable. A control network ( ) is provided that accurately models the plant ( ). The output of the control network ( ) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network ( ), which is the inverse of the control network ( ) to generate a control error signal that is input to a distributed control system ( ) to vary the control inputs to the plant ( ) in order to change the output y(t) to meet the desired output. The control network ( ) is comprised of a first network NET that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer ( ). The output of the residual layer ( ) is input to a hidden layer ( ) which also receives the control inputs to generate a predicted output in an output layer ( ). During back propagation of error, the residual values in the residual layer ( ) are latched and only the control inputs allowed to vary.

A neural network system is provided that models the system in a system model (,) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (,). Input data is processed in a data preprocess step (,) to reconcile the data for input to the system model (,). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (,) which is utilized to control the output control (,). The output control (,) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (,) exceeds a predetermined decision threshold, input by a decision threshold block (,). Additionally, a validity model (,) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (,). This predicts the confidence in the predicted output which is also input to the decision processor (,). The decision processor (,) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (,) can be utilized to train the system model (,).

Method for training and/or testing a neural network with missing and/or incomplete data

Method for operating a neural network with missing and/or incomplete data

A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22). Additionally, a validity model (16) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (12). This predicts the confidence in the predicted output which is also input to the decision processor (20). The decision processor (20) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (10) can be utilized to train the system model (12).

Method and apparatus for operating neural network with missing and/or

A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102). The output of the residual layer (102) is input to a hidden layer (108) which also receives the control inputs to generate a predicted output in an output layer (106). During back propagation of error, the residual values in the residual layer (102) are latched and only the control inputs allowed to vary.

A continuous emission monitoring system for a manufacturing plant (10) includes a control system (16) which has associated therewith a virtual sensor network (18). The network (18) is a predictive network that receives as inputs both control values to the plant (10) and also sensor values. The network (18) is then operable to map the inputs through a stored representation of the plant (10) to output a predicted pollutant sensor level. This predicted pollutant sensor level is essentially the prediction of an actual pollutant sensor level that can be measured by a pollutant sensor (14). The network (18) therefore is a substitute for the pollutant sensor (14), thus providing a virtual sensor. The sensor values from the plant (10) are first processed through a sensor validation system (22). The sensor validation system (22) is operable to determine which of the sensors falls outside of acceptable limits and then substitutes therefor a predicted output, such that the inputs to the virtual sensor network (18) are within acceptable limits.

Virtual continuous emission monitoring system

An internal combustion engine [(360)] is provided with a plurality of sensors to monitor the operation thereof with respect to various temperature measurements, pressure measurements, etc. A predictive model processor [(322)] is provided that utilizes model parameters stored in the memory [(324)] to predict from the sensor inputs a predicted emissions output. The model is trained with inputs provided by the sensor and an actual emissions sensor output. During operation, this predicted output on line [(326)] can be utilized to provide an alarm or to be stored in a history database in a memory [(328)]. Additionally, the internal combustion engine [(260)] can have the predicted emissions output thereof periodically checked to determine the accuracy of the model. This is effected by connecting the output of the engine to an external emissions sensor [(310)] and taking the difference between the actual output and the predicted output to provide an error. This is compared to a threshold and, if the error exceeds the threshold, the predictive model processor [(322)] can be placed in a training operation wherein new model parameters are generated. Additionally, retraining could take place external to the predictive model processor [(322)]. During runtime, a control network [(350)] can be utilized to predict control parameters for minimizing the emissions output.

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Round Rock, TX, United States of America

Ralph Bruce Ferguson