Abstract: The invention concerns a method for determining competing risks for objects following an initial event based on previously measured or otherwise objectifiable training data patterns, in which several signals obtained from a learning capable system are combined in an objective function in such a way that said learning capable system is rendered capable of detecting or forecasting the underlying probabilities of each of the said competing risks.

Abstract: A method is described for improving the prediction accuracy and generalization performance of artificial neural network models in presence of input-output example data containing instrumental noise and/or measurement errors, the presence of noise and/or errors in the input-output example data used for training the network models create difficulties in learning accurately the nonlinear relationships existing between the inputs and the outputs, to effectively learn the noisy relationships, the methodology envisages creation of a large-sized noise-superimposed sample input-output dataset using computer simulations, here, a specific amount of Gaussian noise is added to each input/output variable in the example set and the enlarged sample data set created thereby is used as the training set for constructing the artificial neural network model, the amount of noise to be added is specific to an input/output variable and its optimal value is determined using a stochastic search and optimization technique, namely, gen

Abstract: Disclosed is a method for delivering decision-supported patient data to a clinician to aid the clinician with the diagnosis and treatment of a medical condition. The method including presenting a patient with questions generated by a decision-support module and gathering patient data indicative of the responses to the questions. Each question presented to the patient is based upon the prior questions presented to and the patient data gathered from the patient. Upon receiving the patient data from the client module, evaluating the patient data at the module to generate decision-supported patient data, this supported patient data includes medical condition diagnoses, pertinent medical parameters for the medical condition, and medical care recommendations for the medical condition. At the client module or a clinician's client module, presenting the clinician with this patient data in either a standardized format associated with a progress note or a format selected by the clinician.

Abstract: The invention involves generating and presenting, typically electronically, a number of design alternatives to persons who are participating in the design, selection, or market research exercise. The participants (referred to as “selectors”) transmit data indicative of their preferences among or between the presented design alternatives, and that data is used to derive a new generation of design alternatives or proposals. The new designs are generated through the use of a computer program exploiting a genetic or evolutionary computational technique. The process is repeated, typically for many iterations or cycles.

Abstract: The present invention relates to a system and methodology for extending and making more appropriate the interactive decisions made by automated services by taking into consideration estimates of the time required by users for the cognitive processing of problems, visualizations, and content based on several factors, including the competency and familiarity of the user with the output and nature of the sequences of output, the complexity of the output and overall tasks being considered, and the context of the user. The methods allow automated applications to control the rate at which the applications interact with users. Portions of automated services are controlled in view of limited human processor capabilities in design/operation of such services, and/or visualizations/output from the services (e.g., amount of dwell time provided/considered before a next automated sequence is displayed/invoked).

Abstract: A system is disclosed that provides a goal based learning system utilizing a rule based expert training system to provide a cognitive educational experience. The system provides the user with a simulated environment that presents a business opportunity to understand and solve optimally. Mistakes are noted and remedial educational material presented dynamically to build the necessary skills that a user requires for success in the business endeavor. The system utilizes an artificial intelligence engine driving individualized and dynamic feedback with synchronized video and graphics used to simulate real-world environment and interactions. Multiple “correct” answers are integrated into the learning system to allow individual learning experiences in which navigation through the system is at a pace controlled by the learner.

Abstract: A system and method is provided for identifying useful content in a knowledge repository accessed by a plurality of users. The system and method includes the operation of identifying each unique user that accesses a document in the knowledge repository. Another operation is tracking an amount of time each unique user has a document open to create a set of document open time values. Document usefulness is then determined based on a comparison of the document open time values for the unique users.

Abstract: A hybrid cascade Model-Based Predictive control (MBPC) and conventional control system for thermal processing equipment of semiconductor substrates, and more in particular for vertical thermal reactors is described. In one embodiment, the conventional control system is based on a PID controller. In one embodiment, the MBPC algorithm is based on both multiple linear dynamic mathematical models and non-linear static mathematical models, which are derived from the closed-loop modeling control data by using the closed-loop identification method. In order to achieve effective dynamic linear models, the desired temperature control range is divided into several temperature sub-ranges. For each temperature sub-range, and for each heating zone, a corresponding dynamic model is identified. During temperature ramp up/down, the control system is provided with a fuzzy control logic and inference engine that switches the dynamic models automatically according to the actual temperature.

Abstract: A reconfigurable digital processing system for space includes the utilization of field programmable gate arrays utilizing a hardware centric approach to reconfigure software processors in a space vehicle through the reprogramming of multiple FPGAs such that one obtains a power/performance characteristic for signal processing tasks that cannot be achieved simply through the use of off-the-shelf processors. In one embodiment, for damaged or otherwise inoperable signal processors located on a spacecraft, the remaining processors which are undamaged can be reconfigured through changing the machine language and binary to the field programmable gate arrays to change the core processor while at the same time maintaining undamaged components so that the signal processing functions can be restored utilizing a RAM-based FPGA as a signal processor.

Abstract: A system for operating/observing a monitoring device of a process controller device of/in a remote operator unit. An additional function block is provided in/on the monitoring device, which intervenes in the communication between the monitoring device and the connected operator units, at least partially takes over the functions that are to be executed by one or more of the operator units and processes the information to be displayed on the operator unit in such a way that the information can be directly displayed.

Abstract: Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support systems are trained on the patient data. Patient data are augmented by biochemical test data, or results, where available, to refine performance. The resulting decision-support systems are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, too assess a course of treatment, to identify new diagnostic tests and disease markers, to identify useful therapies, and to provide the decision-support functionality for the test.

Abstract: One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing.

Abstract: An apparatus and method for controlling trajectory of an object (47) to a first predetermined position. The apparatus has an input layer (22) having nodes (22a-22f) for receiving input data indicative of the first predetermined position. First weighted connections (28) are connected to the nodes of the input layer (22). Each of the first weighted connections (28) have a coefficient for weighting the input data. An output layer (26) having nodes (26a-26e) connected to the first weighted connections (28) determines trajectory data based upon the first weighted input data. The trajectory of the object is controlled based upon the determined trajectory data.

Abstract: In an analysis of a set of discrete multidimensional data which can be represented in an array with a topology, where the array that can be mapped to an image space of discrete elements, such as digitized image data, seismic data and audio data, genotype/phenotype classifications are imposed on the topology, and then molecular biological-like processes (annealing, fragmentation, chromatographic separation, fingerprinting, footprinting and filtering) are imposed upon that topology to perceive classifiable regions such as edges. More specifically, an image feature probe constructed of strings of contiguous image fragments of the class of N-grams called linear N-grams, anneals genotypes of topological features by complementary biological-like techniques in the same manner that complex biological systems are analyzed by genetic mapping, sequencing and cloning techniques. For example, molecular biological probes anneal with molecular biological genotypes and then are used to classify those genotypes.

Abstract: A non-linear dynamic predictive device (60) is disclosed which operates either in a configuration mode or in one of three runtime modes: prediction mode, horizon mode, or reverse horizon mode. An external device controller (50) sets the mode and determines the data source and the frequency of data. In prediction mode, the input data are such as might be received from a distributed control system (DCS) (10) as found in a manufacturing process; the device controller ensures that a contiguous stream of data from the DCS is provided to the predictive device at a synchronous discrete base sample time. In prediction mode, the device controller operates the predictive device once per base sample time and receives the output from the predictive device through path (14). In horizon mode and reverse horizon mode, the device controller operates the predictive device additionally many times during base sample time interval. In horizon mode, additional data is provided through path (52).

Abstract: A controller for a switched reluctance machine utilizing a feedforward neural network in combination with either a fuzzy logic controller or a proportional-integral controller to provide output control signals (e.g., turn-ON angle, turn-OFF angle and peak current) for controlling the energization of a switched reluctance machine. In an alternate embodiment, a fuzzy logic controller is utilized by itself to control a switched reluctance machine.

Abstract: Neural computing techniques are used to optimize route selection in a communication network, such as an ATM network. Output measurements of the network are used to provide optimal routing selection and traffic management. Specifically, link data traffic is monitored in the network to obtain traffic history data. An autoregressive backpropagation neural network is trained using the traffic history data to obtain respective predicted traffic profiles for the links. Particular links are then selected for carrying data based on the predicted traffic profiles. A cost function, limits on network parameters such as link cost and cell rate, and other quality of service factors are also considered in selecting the optimal route.

Abstract: One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing.

Abstract: An iterative method enabling a request plan to be established for an observation satellite. A plan consists in a succession of requests which are associated with pluralities of opportunities for satisfying said requests. The plan must also comply with a plurality of constraints. Each iteration k of the iterative method is made up of the following steps: new opportunity is selected; a provisional plan is derived from the preceding plan k-1 as calculated in-the preceding iteration, and from the new opportunity; provisional plan is verified for compliance with said plurality of constraints; the quality of said provisional plan is evaluated; and it is determined whether the provisional plan should be confirmed as plan k as a function of the quality of said provisional plan and of the quality of said preceding plan k-1.

Abstract: An artificial neural network-based system and method for determining desired concepts and relationships within a predefined field of endeavor, including a neural network portion, which neural network portion includes an artificial neural network that has been previously trained in accordance with a set of given training exemplars, a monitor portion associated with the neural network portion to observe the data outputs produced by the previously trained artificial neural network, and a perturbation portion for perturbing the neural network portion to effect changes, subject to design constraints of the artificial neural network that remain unperturbed, in the outputs produced by the neural network portion, the perturbation portion operable such that production of an output by the neural network portion thereafter effects a perturbation of the neural network portion by the perturbation portion, the monitor portion responsive to detection of the data outputs being produced by the previously trained neural networ