747results 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) framework that may be configured to enable users to define neuromorphic network architectures using a unified and unambiguous representation that is both human-readable and machine-interpretable. The framework may be used to define nodes types, node-to-node connection types, instantiate node instances for different node types, and to generate instances of connection types between these nodes. To facilitate framework usage, the HLND format may provide the flexibility required by computational neuroscientists and, at the same time, provides a user-friendly interface for users with limited experience in modeling neurons. The HLND kernel may comprise an interface to Elementary Network Description (END) that is optimized for efficient representation of neuronal systems in hardware-independent manner and enables seamless translation of HLND model description into hardware instructions for execution by various processing modules.

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.

Apparatus and methods for detecting salient features. In one implementation, an image processing apparatus utilizes latency coding and a spiking neuron network to encode image brightness into spike latency. The spike latency is compared to a saliency window in order to detect early responding neurons. Salient features of the image are associated with the early responding neurons. A dedicated inhibitory neuron receives salient feature indication and provides inhibitory signal to the remaining neurons within the network. The inhibition signal reduces probability of responses by the remaining neurons thereby facilitating salient feature detection within the image by the network. Salient feature detection can be used for example for image compression, background removal and content distribution.

An endpoint computer in an enterprise network is configured to detect computer security threat events, such as presence of a computer virus. Upon detection of a threat event, the endpoint computer generates computer security threat data for the threat event. The threat data may include user identifiable data that can be used to identify a user in the enterprise network. The endpoint computer encrypts the user identifiable data prior to sending the threat data to a smart protection network or to an enterprise server where threat data from various enterprise networks are collected for analysis. The endpoint computer may also encrypt an identifier for the threat data and provide the encrypted identifier to the smart protection network and to an enterprise server in the enterprise network. The enterprise server may use the encrypted identifier to retrieve the threat data from the smart protection network to generate user-specific reports.

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).

Apparatus and methods for learning and training in neural network-based devices. In one implementation, the devices each comprise multiple spiking neurons, configured to process sensory input. In one approach, alternate heterosynaptic plasticity mechanisms are used to enhance learning and field diversity within the devices. The selection of alternate plasticity rules is based on recent post-synaptic activity of neighboring neurons. Apparatus and methods for simplifying training of the devices are also disclosed, including a computer-based application. A data representation of the neural network may be imaged and transferred to another computational environment, effectively copying the brain. Techniques and architectures for achieve this training, storing, and distributing these data representations are also disclosed.

The Collateral Management with Blockchain and Smart Contracts Apparatuses, Methods and Systems (“CMBSC”) transforms borrow transaction request inputs via CMBSC components into borrow transaction init notification, borrow transaction sync notification outputs. A borrow transaction request associated with a borrow transaction is obtained. Transaction attributes associated with the borrow transaction are stored in a database. The transaction process optimizer component is notified regarding the borrow transaction. A blockchain sync notification associated with the borrow transaction is obtained from the transaction process optimizer component. The stored transaction attributes associated with the borrow transaction are filtered. A smart contract associated with the borrow transaction is generated. The generated smart contract is sent to a blockchain node of a blockchain network. A smart contract notification associated with the smart contract is received. A push notification regarding the smart contract notification is provided to a user interface component of a user's client.

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Methods for managing memory in a processing system are described whereby memory can be allocated among a plurality of elements and rules configured for each element such that the parallel execution of the spiking networks is most optimal.

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Neuronal network and methods for operating neuronal networks comprise a plurality of units, where each unit has a memory and a plurality of doublets, each doublet being connected to a pair of the plurality of units. Execution of unit update rules for the plurality of units is order-independent and execution of doublet event rules for the plurality of doublets is order-independent.

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 circuit element of a multi-dimensional dynamic adaptive neural network array (DANNA) may comprise a neuron / synapse select input functional to select the circuit element to function as one of a neuron and a synapse. In one embodiment of a DANNA array of such circuit elements, (wherein a circuit element may be digital), a destination neuron may be connected to a first neuron by a first synapse in one dimension a second destination neuron may be connected to the first neuron by a second synapse in a second dimension to form linked columns and rows of neuron / synapse circuit elements. In one embodiment, the rows and columns of circuit elements have read registers that are linked together by signal lines and clocked and controlled so as to output columnar data to an output register when a neuron / synapse data value is stored in the read register.

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.

A method and apparatus for constructing a neuroscience-inspired dynamic architecture (NIDA) for an artificial neural network is disclosed. The method comprises constructing, in one embodiment, an artificial neural network embodiment in a multi-dimensional space in memory such that a neuron is connected by a synapse to another neuron. The neuron and the synapse each have parameters and have features of long-term potentiation and long-term depression. Furthermore, crossover and mutation are employed to select children of parents. Through learning, an initial network may evolve into a different network when NIDA is applied to solve different problems of control, anomaly detection and classification over selected time units. The apparatus comprises in one embodiment a computational neuroscience-inspired artificial neural network with at least one affective network coupled to receive input data from an environment and to output data to the environment.

A method and apparatus for constructing a neuroscience-inspired artificial neural network (NIDA) or a dynamic adaptive neural network array (DANNA) or combinations of substructures thereof comprises one of constructing a substructure of an artificial neural network for performing a subtask of the task of the artificial neural network or extracting a useful substructure based on one of activity, causality path, behavior and inputs and outputs. The method includes identifying useful substructures in artificial neural networks that may be either successful at performing a subtask or unsuccessful at performing a subtask. Successful substructures may be implanted in an artificial neural network and unsuccessful substructures may be extracted from the artificial neural network for performing the task. The method and apparatus supports constructing, using and reusing components and structures of a neuroscience-inspired artificial neural network dynamic architecture in software and a dynamic adaptive neural network array.

A method of emulating the human brain with its thought and rationalization processes is presented here, as well as a method of storing human-like thought. The invention provides for inclusion of psychological profiles, experience and societal position in an electronic emulation of the human brain. This permits a realistic human-like response by that emulation to the people and the interactive environment around it.

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.

A method and apparatus for constructing one of a neuroscience-inspired artificial neural network and a neural network array comprises one of a neuroscience-inspired dynamic architecture, a dynamic artificial neural network array and a neural network array of electrodes associated with neural tissue such as a brain, the method and apparatus having a special purpose display processor. The special purpose display processor outputs a display over a period of selected reference time units to demonstrate a neural pathway from, for example, one or a plurality of input neurons through intermediate destination neurons to an output neuron in three dimensional space. The displayed neural network may comprise neurons and synapses in different colors and may be utilized, for example, to show the behavior of a neural network for classifying hand-written digits between values of 0 and 9 or recognizing vertical / horizontal lines in a grid image of lines.

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.

The system is presented to recognize visual inputs through an optimized convolutional neural network deployed on-board the end user mobile device [8] equipped with a visual camera. The system is trained offline with artificially generated data by an offline trainer system [1], and the resulting configuration is distributed wirelessly to the end user mobile device [8] equipped with the corresponding software capable of performing the recognition tasks. Thus, the end user mobile device [8] can recognize what is seen through their camera among a number of previously trained target objects and shapes.

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 simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. The format is specifically tuned for neural systems and specialized neuromorphic hardware, thereby serving as a bridge between developers of brain models and neuromorphic hardware manufactures.

A circuit element of a multi-dimensional dynamic adaptive neural network array (DANNA) may comprise a neuron / synapse select input functional to select the circuit element to function as one of a neuron and a synapse. In one embodiment of a DANNA array of such circuit elements, (wherein a circuit element or component thereof may be analog or digital), a destination neuron may be connected to a first neuron by a first synapse in one dimension, a second destination neuron may be connected to the first neuron by a second synapse in a second dimension and, optionally, a third destination neuron may be connected to the first neuron by a third synapse. The DANNA may thus form multiple levels of neuron and synapse circuit elements. In one embodiment, multiples of eight inputs may be selectively received by the circuit element selectively functioning as one of a neuron and a synapse. The dynamic adaptive neural network array (DANNA) may comprise a special purpose processor for performing one of a control, anomaly detection and classification application and may comprise a first structure connected to a neuroscience-inspired dynamic artificial neural network (NIDA), comprise substructures thereof or be combined with other neural networks.

A method is provide for providing sensors for a machine. The method may include obtaining data records including data from a plurality of sensors for the machine and determining a virtual sensor corresponding to one of the plurality of sensors. The method may also include establishing a virtual sensor process model of the virtual sensor indicative of interrelationships between at least one sensing parameters and a plurality of measured parameters based on the data records and obtaining a set of values corresponding to the plurality of measured parameters. Further, the method may include calculating the values of the at least one sensing parameters substantially simultaneously based upon the set of values corresponding to the plurality of measured parameters and the virtual sensor process model and providing the values of the at least one sensing parameters to a control system.

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.