56565 results about "Neural network nn" patented technology

We describe a method of reinforcement learning for a subject system having multiple states and actions to move from one state to the next. Training data is generated by operating on the system with a succession of actions and used to train a second neural network. Target values for training the second neural network are derived from a first neural network which is generated by copying weights of the second neural network at intervals.

A color management method/apparatus generates image color matching and International Color Consortium (ICC) color printer profiles using a reduced number of color patch measurements. Color printer characterization, and the generation of ICC profiles usually require a large number of measured data points or color patches and complex interpolation techniques. This invention provides an optimization method/apparatus for performing LAB to CMYK color space conversion, gamut mapping, and gray component replacement. A gamut trained network architecture performs LAB to CMYK color space conversion to generate a color profile lookup table for a color printer, or alternatively, to directly control the color printer in accordance with the a plurality of color patches that accurately. represent the gamut of the color printer. More specifically, a feed forward neural network is trained using an ANSI/IT-8 basic data set consisting of 182 data points or color patches, or using a lesser number of data points such as 150 or 101 data points when redundant data points within linear regions of the 182 data point set are removed. A 5-to-7 neuron neural network architecture is preferred to perform the LAB to CMYK color space conversion as the profile lookup table is built, or as the printer is directly controlled. For each CMYK signal, an ink optimization criteria is applied, to thereby control ink parameters such as the total quantity of ink in each CMYK ink printed pixel, and/or to control the total quantity of black ink in each CMYK ink printed pixel.

The present invention provides an apparatus and method for high resolution spectroscopy (approximately 10 picometer wavelength resolution) using a tunable optical filter (TOF) for analyzing a formation fluid sample downhole and at the surface to determine formation fluid parameters. The analysis comprises determination of gas oil ratio, API gravity and various other fluid parameters which can be estimated after developing correlations to a training set of samples using a neural network or a chemometric equation.

Systems and methods are provided for training an artificial intelligence system including the use of one or more human subject responses to stimuli as input to the artificial intelligence system. Displays are oriented to the human subjects to present the stimuli to the human subjects. Detectors monitor the reaction of the human subjects to the stimuli, the detectors including at least motion detectors, the detectors providing an output. An analysis system is coupled to receive the output of the detectors, the analysis system provides an output corresponding to whether the reaction of the human subjects was positive or negative. A neural network utilizes the output of the analysis system, generating a positive weighting for training of the neural network when the output of the analysis system was positive, and a negative weighting for training of the neural network when the output of the analysis system was negative.

The invention discloses a convolution neural network parallel processing method based on a large-scale high-performance cluster. The method comprises the steps that (1) a plurality of copies are constructed for a network model to be trained, model parameters of all the copies are identical, the number of the copies is identical with the number of nodes of the high-performance cluster, each node is provided with one model copy, one node is selected to serve as a main node, and the main node is responsible for broadcasting and collecting the model parameters; (2) a training set is divided into a plurality of subsets, the training subsets are issued to the rest of sub nodes except the main mode each time to conduct parameter gradient calculation together, gradient values are accumulated, the accumulated value is used for updating the model parameters of the main node, and the updated model parameters are broadcast to all the sub nodes until model training is ended. The convolution neural network parallel processing method has the advantages of being capable of achieving parallelization, improving the efficiency of model training, shortening the training time and the like.

A neural network type of apparatus is provided to detect an internal state of a secondary battery implemented in a battery system. The apparatus comprises a detecting unit, producing unit and estimating unit. The detecting unit detects electric signals indicating an operating state of the battery. The producing unit produces, using the electric signals, an input parameter required for estimating the internal state of the battery. The input parameter reflects calibration of a present charged state of the battery which is attributable to at least one of a present degraded state of the battery and a difference in types of the battery. The estimating unit estimates an output parameter indicating the charged state of the battery by applying the input parameter to neural network calculation.

Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing.

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 invention provides a method for monitoring hydrology and water quality of a river basin under influence of water projects based on Internet of Things. According to the method, various fixed and flowing sensors are additionally mounted so as to obtain real-time hydrology and water quality data of important water areas, in combination with a video technology, monitoring on a spot environment is achieved, the monitoring information is transmitted through the Internet of Things, intelligent prediction for hydrology and water quality of the river basin is carried out through a neural network, and finally, critical applications, such as current situation assessment, tendency estimation, implementation effect estimation and extreme event handling, are achieved through an expert system. The method provided by the invention can provide real-time, reliable and complete hydrology and water quality information for the river basin under influence of water projects, and can achieve cross-regional and multi-machine type integrated remote protection for the river basin.

A fault detection system for use with a solar hot water system may include a data acquisition module which may, in turn, include a plurality of sensors. Input data may include a sensed condition. The system may also include a neural network to receive the input data which may be a multi-layer hierarchical adaptive resonance theory (ART) neural network. The neural network may perform an analysis on the input data to determine existence of a fault or a condition indicative of a potential fault. The fault and the condition indicative of the potential fault are prioritized according to the analysis performed by the neural network. A warning output relating to the fault and the condition indicative of the potential fault is generated responsive to the analysis, and is displayed on the user interface.

A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.

Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing.