Class / Patent application number | Description | Number of patent applications / Date published |
706023000 | Control | 28 |
20080275831 | Bipedal Walking Simulation - An artificial multiped is constructed (either in simulation or embodied) in such a way that its natural body dynamics allow the lower part of each leg to swing naturally under the influence of gravity. The upper part of each leg is actively actuated in the sagittal plane. The necessary input to drive the above-mentioned actuators is derived from a neural network controller. The latter is arranged as two bi-directionally coupled chains of neural oscillators, the number of which equals twice that of the legs to be actuated. Parameter optimisation of the controllers is achieved by evolutionary computation in the form of a genetic algorithm. | 11-06-2008 |
20090070282 | METHOD AND SYSTEM OF MONITORING PROGNOSTICS - A neural network learns the operating modes of a system being monitored under normal operating conditions. Anomalies can be automatically detected and learned. A control command can be issued or an alert can be issued in response thereto. | 03-12-2009 |
20090271344 | Method for computer-aided control or regualtion of a technical system - A method for computer-aided control of any technical system is provided. The method includes two steps, the learning of the dynamic with historical data based on a recurrent neural network and a subsequent learning of an optimal regulation by coupling the recurrent neural network to a further neural network. The recurrent neural network has a hidden layer comprising a first and a second hidden state at a respective time point. The first hidden state is coupled to the second hidden state using a matrix to be learned. This allows a bottleneck structure to be created, in that the dimension of the first hidden state is smaller than the dimension of the second hidden state or vice versa. The autonomous dynamic is taken into account during the learning of the network, thereby improving the approximation capacity of the network. The technical system includes a gas turbine. | 10-29-2009 |
20100030716 | System, Apparatus and Methods for Augmenting Filter with Adaptive Element - A system in accordance with the invention uses an adaptive element to augment a filter for tracking an observed system. The adaptive element only requires a single neural network and does not require an error observer. The adaptive element provides robustness to parameter uncertainty and unmodeled dynamics present in the observed system for improved tracking performance over the filter alone. The adaptive element can be implemented with a linearly parameterized neural network, whose weights are adapted online using error residuals generated from the Filter. Boundedness of the signals generated by the system can be proven using Lyapunov's direct method and a backstepping argument. A related apparatus and method are also disclosed. | 02-04-2010 |
20100169254 | SYSTEM AND METHOD FOR CALIBRATING RADIO FREQUENCY POWER OF COMMUNICATION DEVICES - A radio frequency (RF) calibrating system and a method for calibrating RF power of communication devices are provided. The method collects RF signals transmitted from the communication devices, and generates a group of training samples by retrieving measurement data from the RF signals. The method further constructs a neural network according to the group of training samples, calibrate RF power of the communication devices using the neural network, and generate corresponding calibration results of the RF power. In addition, the method generates a frequency spectrum of the RF power according to the calibration results of the RF power, and displays the frequency spectrum on a display device of the RF calibrating system. | 07-01-2010 |
20100217733 | NEURON DEVICE, NEURAL NETWORK DEVICE, FEEDBACK CONTROL DEVICE, AND INFORMATION RECORDING MEDIUM - In order to utilize variable neuron thresholds and extended Hebb's rule in a neural network for proper control, a neuron device ( | 08-26-2010 |
20110125687 | Method for hammerstein modeling of steam generator plant - The method for Hammerstein modeling of a steam generator plant includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). Particle swarm optimization (PSO), typically known for its heuristic search capabilities, is used for estimating the parameters of the RBFNN. Parameters of the linear part are estimated using a numerical algorithm for subspace state-space system identification (N4SID). | 05-26-2011 |
20110145180 | Diagnostic Method for a Process Automation System - A diagnostic method for a process automation system composed of at least one field device, a control unit, and at least one fieldbus. The following method steps are included: in a learning phase raw data of measured variables, raw data of manipulated variables and/or raw data of state variables are stored. The field devices or the processes as input variables are registered and stored normalized. Moreover, in the learning phase at least one parameter, a measuring condition, at least one parameter of a process state and/or at least one parameter of a field device state as output variable predetermines, which corresponding output variables are the input variables associated with that which is stored, during the learning phase. A neural network formed by the input variables and the associated with output variables, in the learning phase are the causal relationships between the ascertained input variables and the corresponding, specified output variables by a transfer function of the neural network ascertained and stored, in an operating phase is by means of the transfer function from the current raw data the field devices as input variables at least one change of the current measuring condition, the current process state and/or the current field device state ascertained. | 06-16-2011 |
20120117011 | CONTROL SYSTEM FOR PLANT - A control system for a plant e.g. as a non-linear system, which is capable of properly suppressing interaction occurring between a plurality of control inputs and a plurality of controlled variables, thereby making it possible to properly control the controlled variables and easily design the control system. In the control system, each of a plurality of interaction suppression parameters for correcting the control inputs, respectively, such that the interaction is suppressed is calculated using a neural network constructed by using, out of the plurality of control inputs, a control input other than a control input corrected by a calculated interaction suppression parameter, as an input, and the interaction suppression parameter as an output. | 05-10-2012 |
20120323831 | METHOD FOR STORING DATA IN MEMORY - A method for storing data in a memory may include when the data may be obtained under a variable condition, determining a cell corresponding to an area of the variable condition from an entire memory area, storing the data in the cell and dividing the cell storing the data into a plurality of cells, and whenever a new data may be obtained under a new variable condition, determining a new cell corresponding to an area of the new variable condition under which the new data may be obtained, from the plurality of cells of the entire memory area and repeating storing of the new data and dividing of the new cell. | 12-20-2012 |
20130018831 | SOOTBLOWING OPTIMIZATION FOR IMPROVED BOILER PERFORMANCE - A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers. | 01-17-2013 |
20130073492 | ELEMENTARY NETWORK DESCRIPTION FOR EFFICIENT IMPLEMENTATION OF EVENT-TRIGGERED PLASTICITY RULES IN NEUROMORPHIC SYSTEMS - 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 software and hardware engines are optimized to take into account short-term and long-term synaptic plasticity in the form of LTD, LTP, and STDP. | 03-21-2013 |
20130325773 | STOCHASTIC APPARATUS AND METHODS FOR IMPLEMENTING GENERALIZED LEARNING RULES - Generalized learning rules may be implemented. A framework may be used to enable adaptive signal processing system to flexibly, combine different learning rules (supervised, unsupervised, reinforcement learning) with different methods (online or batch learning). The generalized learning framework may employ time-averaged performance function as the learning measure thereby enabling modular architecture where learning tasks are separated from control tasks, so that changes in one of the modules do not necessitate changes within the other. The generalized learning apparatus may be capable of implementing several learning rules concurrently based on the desired control application and without requiring users to explicitly identify the required learning rule composition for that application. | 12-05-2013 |
20130325774 | LEARNING STOCHASTIC APPARATUS AND METHODS - Generalized learning rules may be implemented. A framework may be used to enable adaptive signal processing system to flexibly combine different learning rules (supervised, unsupervised, reinforcement learning) with different methods (online or batch learning). The generalized learning framework may employ non-associative transform of time-averaged performance function as the learning measure, thereby enabling modular architecture where learning tasks are separated from control tasks, so that changes in one of the modules do not necessitate changes within the other. The use of non-associative transformations, when employed in conjunction with gradient optimization methods, does not bias the performance function gradient, on a long-term averaging scale and may advantageously enable stochastic drift thereby facilitating exploration leading to faster convergence of learning process. When applied to spiking learning networks, transforming the performance function using a constant term, may lead to non-associative increase of synaptic connection efficacy thereby providing additional exploration mechanisms. | 12-05-2013 |
20140019392 | INTELLIGENT MODULAR ROBOTIC APPARATUS AND METHODS - Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism. | 01-16-2014 |
20140250036 | APPARATUS AND METHODS FOR EVENT-TRIGGERED UPDATES IN PARALLEL NETWORKS - 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 software and hardware engines are optimized to take into account short-term and long-term synaptic plasticity in the form of LTD, LTP, and STDP. | 09-04-2014 |
20140250037 | METHODS FOR MEMORY MANAGEMENT IN PARALLEL NETWORKS - 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. | 09-04-2014 |
20150039545 | SYSTEMS, METHODS AND DEVICES FOR VECTOR CONTROL OF PERMANENT MAGNET SYNCHRONOUS MACHINES USING ARTIFICIAL NEURAL NETWORKS - An example method for controlling an AC electrical machine can include providing a PWM converter operably connected between an electrical power source and the AC electrical machine and providing a neural network vector control system operably connected to the PWM converter. The control system can include a current-loop neural network configured to receive a plurality of inputs. The current-loop neural network can be configured to optimize the compensating dq-control voltage. The inputs can be d- and q-axis currents, d- and q-axis error signals, predicted d- and q-axis current signals, and a feedback compensating dq-control voltage. The d- and q-axis error signals can be a difference between the d- and q-axis currents and reference d- and q-axis currents, respectively. The method can further include outputting a compensating dq-control voltage from the current-loop neural network and controlling the PWM converter using the compensating dq-control voltage. | 02-05-2015 |
20150074026 | APPARATUS AND METHODS FOR EVENT-BASED PLASTICITY IN SPIKING NEURON NETWORKS - Event based communication in a spiking neuron network may be provided. The network may comprise units communicating by spikes via synapses. Responsive to a spike generation, a unit may be configured to update states of outgoing synapses. The spikes may communicate a payload data. The data may comprise one or more bits. The payload may be stored in a buffer of a pre-synaptic unit and be configured to accessed by the post-synaptic unit. Spikes of different payload may cause different actions by the recipient unit. Sensory input spikes may cause postsynaptic response and trigger connection efficacy update. Teaching input may be used to modulate plasticity. | 03-12-2015 |
20150142714 | DYNAMIC LIGHTING SYSTEM - A dynamic lighting system may comprise a base node having a broadcast range, a plurality of lights being operatively associated with a set of basic nodes, and a network brain configured to communicate with the base node and store a node list with the unique identifiers for the set of basic nodes. The set of basic nodes may include local nodes in the broadcast range and remote nodes beyond the broadcast range. The network brain may be configured to send a broadcast message that is communicated to local nodes within the broadcast range and remote nodes beyond the broadcast range. | 05-21-2015 |
20150142715 | NEURAL NETWORK FREQUENCY CONTROL AND COMPENSATION OF CONTROL VOLTAGE LINEARITY - Systems and methods of using an artificial neural network processing module to compensate a control voltage and create a linear output response for an electronic oscillator to produce a target frequency. The artificial neural network processing module includes one or more neurons which receive one or more inputs corresponding to the control voltage. The artificial neural network processing module is configured to provide a correction based at least in part on the control voltage and pre-calculated DAC values. The pre-calculated DAC values are determined in part by predetermined or predefined pull ranges and linear control voltage transfer functions. The artificial neural network processing module can preferably achieve a control voltage tuning linearity better than 0.5% linearity over an entire tuning range of ±−75 ppm. | 05-21-2015 |
20150301510 | Controlling a Target System - For controlling a target system, operational data of a plurality of source systems are used. The data of the source systems are received and are distinguished by source system specific identifiers. By a neural network, a neural model is trained on the basis of the received operational data of the source systems taking into account the source system specific identifiers, where a first neural model component is trained on properties shared by the source systems and a second neural model component is trained on properties varying between the source systems. After receiving operational data of the target system, the trained neural model is further trained on the basis of the operational data of the target system, where a further training of the second neural model component is given preference over a further training of the first neural model component. The target system is controlled by the further trained neural network. | 10-22-2015 |
20150302297 | Magnetic Resonance Fingerprinting (MRF) Serial Artificial Neural Network (ANN) Sequence Design - Example apparatus and methods employ an artificial neural network (ANN) to automatically design magnetic resonance (MR) pulse sequences. The ANN is trained using transverse magnetization signal evolutions having arbitrary initial magnetizations. The trained up ANN may then produce an array of signal evolutions associated with a pulse sequence having user selectable pulse sequence parameters that vary in degrees of freedom associated with magnetic resonance fingerprinting (MRF). Efficient and accurate approaches are provided for predicting user controllable MR pulse sequence settings including, but not limited to, acquisition period and flip angle (FA). The acquisition period and FA may be different in different sequence blocks in the pulse sequence produced by the ANN. Predicting user controllable MR pulse sequence settings for both conventional MR and MRF facilitates achieving desired signal characteristics from a signal evolution produced in response to an automatically generated pulse sequence. | 10-22-2015 |
20150317556 | ADAPTIVE QUICK RESPONSE CONTROLLING SYSTEM FOR SOFTWARE DEFINED STORAGE SYSTEM FOR IMPROVING PERFORMANCE PARAMETER - An adaptive quick response controlling system for a software defined storage (SDS) system to improve a performance parameter is disclosed. The system includes: a traffic monitoring module, for acquiring an observed value of the performance parameter in a storage node; an adaptive dual neural module, for learning best configurations of a plurality of storage devices in the storage node under various difference values between the observed values and a specified value of the performance parameter from historical records of configurations of the storage devices and associated observed values, and providing the best configurations when a current difference value is smaller than a threshold value; and a quick response control module, for changing a current configuration of the storage devices in the storage node as the best configuration of the storage devices provided from the adaptive dual neural module if the current difference value is not smaller than the threshold value. | 11-05-2015 |
20150324687 | INTELLIGENT MODULAR ROBOTIC APPARATUS AND METHODS - Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism. | 11-12-2015 |
20160048753 | Multiplicative recurrent neural network for fast and robust intracortical brain machine interface decoders - A brain machine interface (BMI) to control a device is provided. The BMI has a neural decoder, which is a neural to kinematic mapping function with neural signals as input to the neural decoder and kinematics to control the device as output of the neural decoder. The neural decoder is based on a continuous-time multiplicative recurrent neural network, which has been trained as a neural to kinematic mapping function. An advantage of the invention is the robustness of the decoder to perturbations in the neural data; its performance degrades less—or not at all in some circumstances—in comparison to the current state decoders. These perturbations make the current use of BMI in a clinical setting extremely challenging. This invention helps to ameliorate this problem. The robustness of the neural decoder does not come at the cost of some performance, in fact an improvement in performance is observed. | 02-18-2016 |
20160086077 | SELF-TIMED, EVENT-DRIVEN NEUROSYNAPTIC CORE CONTROLLER - Embodiments of the invention relate to a system for controlling program execution. The system comprises an event-based core controller including a set of state-preserving elements. The core controller starts and stops the program execution based on one or more control signals. For each instruction of the program, the core controller triggers a target component to execute the instruction by generating and sending an instruction and/or a trigger pulse to the target component. | 03-24-2016 |
20180024508 | SYSTEM MODELING, CONTROL AND OPTIMIZATION | 01-25-2018 |