743results about "Computer simulations" patented technology

Provided herein is a system for creating, modifying, deploying and running intelligent systems by combining and customizing the function and operation of reusable component modules arranged into neural processing graphs which direct the flow of signals among the modules, analogous in part to biological brain structure and operation as compositions of variations on functional components and subassemblies.

A system and a method provide a photovoltaic system which regenerates the output characteristics of the photovoltaic at different ambient condition with high precision under all environmental conditions. The photovoltaic system includes a photovoltaic array, a buck / boost converter, a DC link capacitor to connect the buck / booster converter to a load / inverter, an adaptive network-based fuzzy inference maximum power point tracking controller, a voltage control loop, a proportional integral controller to maintain the output voltage of the photovoltaic array to the reference voltage by adjusting the duty ratio of buck / boost converter.

Apparatus and methods for high-level neuromorphic network description (HLND) using tags. The framework may be used to define nodes types, define node-to-node connection types, instantiate node instances for different node types, and / or generate instances of connection types between these nodes. The HLND format may be used to define nodes types, define node-to-node connection types, instantiate node instances for different node types, dynamically identify and / or select network subsets using tags, and / or generate instances of one or more connections between these nodes using such subsets. To facilitate the HLND operation and disambiguation, individual elements of the network (e.g., nodes, extensions, connections, I / O ports) may be assigned at least one unique tag. The tags may be used to identify and / or refer to respective network elements. The HLND kernel may comprises an interface to Elementary Network Description.

A neural network simulation represents components of neurons by finite state machines, called sectors, implemented using look-up tables. Each sector has an internal state represented by a compressed history of data input to the sector and is factorized into distinct historical time intervals of the data input. The compressed history of data input to the sector may be computed by compressing the data input to the sector during a time interval, storing the compressed history of data input to the sector in memory, and computing from the stored compressed history of data input to the sector the data output from the sector.

A method and system for implementing a neuro-controller. One example of a neuro-controller is a brain-like stochastic search. Another example is a neuro-controller for controlling a hypersonic aircraft. Using a variety of learning techniques, the method and system provide adaptable control of external devices (e.g., airplanes, plants, factories, and financial systems).

Systems and methods for efficient implementation of a convolutional layer of a convolutional neural network are disclosed. In one aspect, weight values of kernels in a kernel stack of a convolutional layer can be reordered into a tile layout with tiles of runnels. Pixel values of input activation maps of the convolutional layer can be reordered into an interleaved layout comprising a plurality of clusters of input activation map pixels. The output activation maps can be determined using the clusters of the input activation map pixels and kernels tile by tile.

A system and method for providing hazardous incident decision support and training includes a user interface component that receives situation definition data, a hazard assessment component and a decision aid. Decision support advice and decision prompts are presented by the system in response to the situation definition interface and hazard assessment. The hazard assessment and expert advice of the system are updated with elapsed time. In another embodiment of the present invention, graphical user interfaces are provided to display a set of menu entries wherein individual menu entries represent a hazardous incident characteristic. The user may select one or more of the menu entries to create a situation definition. Such definitions may be used, for example, to identify a hazardous agent based upon user-inputted signs and symptoms data.

Provided is a video encoding / decoding technique for improving the compression efficiency by reducing the motion vector code amount. In a video decoding process, the prediction vector calculation method is switched from one to another in accordance with a difference between predetermined motion vectors among a plurality of motion vectors of a peripheral block of a block to be decoded and already decoded. The calculated prediction vector is added to a difference vector decoded from an encoded stream so as to calculate a motion vector. By using the calculated motion vector, the inter-image prediction process is executed.

Provided herein is a system for creating, modifying, deploying and running intelligent systems by combining and customizing the function and operation of reusable component modules arranged into neural processing graphs which direct the flow of signals among the modules, analogous in part to biological brain structure and operation as compositions of variations on functional components and subassemblies.

A system and method responsive to input stimuli is provided by incorporating a computer software program, hardware processing engine, or a specialized ASIC chip processor apparatus to capture concurrent inputs that are responsive to training stimulation, store a model representing a synthesis of the captured inputs, and use the stored model to generate outputs in response to real-world stimulation. Human user forced-choice approval / disapproval generated descriptions and decisions may be dynamically mapped with conventionally presented information and sensor and control data. The model mapping is stored into and out of a conventional mass storage device, such as is used in a relational database for use in generating a response to the stimuli. By accessing commonly stored mappings, the system can be incorporated into a mixture of multiple domains and disciplines of users and can create a common understanding of knowledge and design concept contained within it through mutual interaction, and subsequent automatic modifications to a common relational database. The system and method is applicable to conventional storage and presentation devices, making it easily incorporated into a variety of commercial products, utilizing current commercial human-machine interfaces (e.g. Human-Machine Interface graphical user interface, or Graphical User Interface) and current mass storage devices. The system uses N-dimensional descriptions of observations and concepts in an infinitely expandable space, embracing elements of human thought. This allows the user to tailor this system to control operation of automated devices and appliances to reflect the individual's wishes and desires as a dynamic representation and mapping of user descriptions and decisions with information, sensor data, and device controls.

A device, system, and method is provided for storing a sparse neural network. A plurality of weights of the sparse neural network may be obtained. Each weight may represent a unique connection between a pair of a plurality of artificial neurons in different layers of a plurality of neuron layers. A minority of pairs of neurons in adjacent neuron layers are connected in the sparse neural network. Each of the plurality of weights of the sparse neural network may be stored with an association to a unique index. The unique index may uniquely identify a pair of artificial neurons that have a connection represented by the weight. Only non-zero weights may be stored that represent connections between pairs of neurons (and zero weights may not be stored that represent no connections between pairs of neurons).

A neural network development and data analysis tool provides significantly simplified network development through use of a scripted programming language, such as Extended Markup Language, or a project “wizard.” The system also provides various tools for analysis and use of a trained artificial neural network, including three-dimensional views, skeletonization, and a variety of output module options. The system also provides for the possibility of autonomous evaluation of a network being trained by the system and the determination of optimal network characteristics for a given set of provided data.

The invention discloses a life prediction method of accelerated life test based on grey RBF neural network. An original curve of reliability and failure time is constructed by collecting test data; class ratio test is conducted on failure time data; a curve of reliability and accumulated failure time is constructed; three layers of RBF artificial neural network are established; RBF artificial neural network is trained; the well-trained neural network is used for prediction; and finally the prediction value of the dummy accumulated failure time obtained by prediction is reduced so as to obtain the life information of the products under normal stress. The method has no need of establishing physical accelerator model and resolving complex multivariate likelihood equation set, thereby avoiding the introduction of system error in the life prediction, solving the problem of needing a large number of training samples for artificial neural network modeling in accelerated life test, also being applied to small sample test data, and facilitating the application in actual engineering. Compared with the existing BP neural network prediction method, the life prediction precision is obviously improved.