18 results about "Neural network topology" patented technology

Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology into an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component parameter values for the analog components.

The present application relates to the technical field of medical data analysis, in particular to a spatiotemporal feature quantitative evaluation method for Parkinson's disease motor symptoms, comprising: deploying an inertial measurement unit to collect three-dimensional acceleration angular velocity magnetic field data, constructing a human body connection structure to generate a connection neural network topology, calculating a trajectory direction angle and angular velocity change adaptive weighting to judge stability, aggregating adjacent features to form a multi-round convolution propagation to form a spatiotemporal fusion motion feature set, identifying tremor gait amplitude according to a time sequence multi-head attention to extract a time sequence feature mode, and calculating tremor gait coordination to generate a Parkinson's disease motor symptom quantitative evaluation result, wherein, in the present application, a sensor network motion data topology connection is constructed, multi-dimensional part information is fused to capture limb coordination, adaptive weight decay and connection convolution deep aggregation features are used to maintain time sequence stability and capture long-range associations, and multi-head attention is combined to distinguish tremor frequency and gait changes, thereby improving the consistency of Parkinson's symptom recognition and evaluation.

The invention relates to an optical neural network topology adaptive mode division multiplexing communication system and a training method, and the method provided by the invention is applied to a mode division multiplexing communication system, and is used for solving the problem that the transmission or calculation performance is reduced due to dynamic coupling crosstalk of a spatial mode caused by environmental disturbance. The method comprises the following steps: monitoring the output of an optical neural network in real time, and generating a state matrix representing mode crosstalk; extracting matrix features to construct an environment vector; through a pre-trained deep reinforcement learning network, a reconstruction action for controlling the adjustable photonic device is decided and generated according to the vector; the driving device dynamically adjusts the network physical topology to compensate crosstalk; and finally, optimizing the strategy network on line based on the reconstructed performance evaluation result to form a closed loop. According to the method, the optical neural network in the mode division multiplexing has online self-adaptive capability, the dynamic mode crosstalk can be continuously inhibited, and the stability and high performance of the system in actual deployment are guaranteed.

The invention discloses a method for predicting the optimal rotating speed of a motor by optimizing a neural network based on a whale optimization algorithm, and relates to the technical field of whale optimization algorithms. The method comprises the following steps: inputting data and an optimal rotating speed, and carrying out normalization processing on the data; designing a BP neural network topological structure, and initializing parameters of the BP neural network topological structure; initializing parameters of a whale optimization algorithm, improving the whale optimization algorithm by introducing a weight strategy and a nonlinear convergence factor, and optimizing the BP neural network by improving the whale optimization algorithm; outputting an optimal weight and a threshold value of the BP neural network, and carrying out a training test; and constructing a model for predicting the optimal rotating speed by optimizing the BP neural network. According to the method, the whale optimization algorithm is improved based on adaptive weight and nonlinear convergence, the optimization precision and the optimization speed are improved, errors are reduced, the BP neural network is optimized by using the improved whale optimization algorithm, and the accuracy of predicting the optimal rotating speed of the motor is improved.

Systems and methods herein disclose technology, architectures) and constructs of artificial (machine) brains having the feature of separated hardware and software wherein their full functionality is defined by software only. Implementations not only replicate all known functions of a human brain including consciousness but also establish universal, novel construction and characteristics of software-defined brains realizing any possible, known or yet unknown functionality of an intelligent computer. Additional aspects disclose classes of different Neural Network (NN) topologies, based on the universal representation of a software-defined nerons an d / or software-defined neural networks to implement any kind of known or yet unkno w n neural network via software implementations embodied on any universal hardware, which enable subjective and objective phenomena utilized for processing machine consciousness and machine subconsciousness. Various illustrative functional complexes are set forth via the disclosure of a. new class of neural networks, namely the Heterogeneous Hierarchical Model (HHM).

A transmission shaft life optimization method based on a digitalized generation structure comprises the steps that according to original geometry and material attributes, after the rigidity and fatigue life of an original shaft are obtained through finite element reference analysis, a preliminary CAD model is generated through neural network topology; and performing iterative finite element performance evaluation to obtain an optimization model of which the rigidity and the service life meet the requirements for manufacturing. According to the invention, through topology generation driven by the neural network and a multi-objective optimization strategy, rigidity and strength maintenance of the transmission shaft is realized, and structural parameters are automatically and intelligently regulated and controlled through a required life value input by a user, so that light weight is realized, and a test period is shortened.

The invention relates to the technical field of neural network topology data processing, in particular to a public resource transaction big data analysis method based on heterogeneous data and intelligent portray.The method comprises the steps that the data of the public resource transaction big data are analyzed on the basis of the equity association strength between transaction subjects in the heterogeneous data, the equipment feature consistency and the spherical distance of the geographic position information of the transaction subjects; calculating a static space correlation degree between any two transaction subjects; according to the static space correlation degree and the Euclidean distance between the current feature vector and the historical feature mean vector, calculating a comprehensive risk index between transaction subjects in the current bid; the state transition of the transaction subject portrait label is judged through the comprehensive risk index, so that the accuracy of abnormal behavior recognition in public resource transaction can be effectively improved.

Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology to an equivalent analog network of analog components including a plurality of operational amplifiers and a plurality of resistors. Each operational amplifier represents an analog neuron of the equivalent analog network, and each resistor represents a connection between two analog neurons. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection. The method also includes generating a resistance matrix for the weight matrix. Each element of the resistance matrix corresponds to a respective weight of the weight matrix and represents a resistance value.

A neural network topology is selected by generating a super-neural network embedding one or more of a plurality of candidate neural networks having different topologies, and generating at least one learnable transform comprising a plurality of trainable weights. At least one of the plurality of candidate neural networks is generated at least in part by applying the at least one learnable transform to the super-neural network. A candidate neural network is selected from among the plurality of candidate neural networks having different topologies for deployment based on performance metrics and topological constraints.

The invention relates to the technical field of actuator service life early warning, and particularly discloses an electric actuator part service life early warning system based on vibration-torque signal fusion, which comprises a data processing center connected with a sensor group, and the data processing center is configured to execute the following steps: responding to a real-time operation state of an electric actuator, and sending the real-time operation state of the electric actuator; and collecting an original vibration signal, an original torque signal and a multi-physics field auxiliary signal, and generating calibrated multi-physics field data based on multi-physics field data cross validation logic. According to the invention, a cross validation and self-calibration mechanism of multi-physical field data is utilized, the problem of false alarm caused by sensor drift in a severe environment is effectively solved, and the authenticity and reliability of input data are ensured; secondly, by sensing the fluctuation intensity of the working condition and adaptively adjusting the neural network topology structure, the degradation features are accurately captured under the dynamic load, and the interference of the non-stable working condition on fault feature extraction is eliminated.

The invention discloses a metal pipeline corrosion and anticorrosive coating stripping test system, and relates to the technical field of metal pipeline detection, the system comprises: an acquisition unit configured to acquire multi-dimensional test data in real time; the first processing unit is used for constructing a grading test evaluation model; the second processing unit is used for mapping the multi-dimensional test data into feature vectors of nodes and edges; the third processing unit is used for carrying out distributed collaborative optimization and updating and optimizing parameters of each edge side test model; and the output unit is configured to obtain a grading test result according to the optimized model. According to the invention, by introducing a collaborative mechanism of multi-dimensional test perception, hierarchical evaluation modeling, graph neural network topological association and distributed collaborative optimization, high-precision, hierarchical and spatial coupling perception and test evaluation of metal pipeline corrosion and anticorrosive coating stripping are realized.

Some implementations provide devices, systems and / or methods for quantifying vulnerability of an artificial neural network (ANN) to poisoning attacks. Some implementations provide devices, systems and / or methods for reducing vulnerability of an artificial neural network (ANN) to poisoning attacks. Some implementations provide devices, systems and / or methods for detecting poisoning attacks in an ANN. An ANN is trained to generate inferences based on a function.

This invention relates to the field of neural network computing technology and discloses a mapping method for neural network computing in a heterogeneous environment. The method includes: parsing the neural network computation graph to extract the topological convergence degree of operator nodes and the asynchronous dwell integral of tensors; monitoring the transient operational deviation rate of computing resource units to generate a performance degradation gradient; filtering operator nodes whose convergence degree and integral exceed limits, and using the inactive period formed by the synchronization time difference of predecessor branches as a timing mask to map the filtered operator nodes to computing resource units in the performance recovery period. This invention utilizes the inherent asynchronous waiting gap of the neural network topology to mask the hardware physical temperature control recovery period, eliminates the local memory backpressure caused by asynchronous tensor stacking, and makes the global performance of heterogeneous computing resources approach the sum of the theoretical physical peak values ​​of the hardware.

A semiconductor device (SoC) (1) and computer-implemented method for deterministic artificial intelligence supervision. It incorporates a secure port (8) in an inert state to ensure subordination by injecting a base truth. An AECM module (3) audits the integrity of physical memory using hash trees on the hot path, repairing tensors on the fly with no latency. An AECM module (4) vetoes semantic hallucinations by calculating spatial divergences in hardware and consolidating neuroplasticity in parallel search associative memories (4a) without retraining software. A forensic module (5) extracts the causal triad and cross-references it with a logical topological map, recording the exact node and layer responsible for the failure in immutable storage to provide full explainability.

This invention relates to the field of logistics robot technology, and in particular to a visual perception-based intelligent logistics robot picking method and system. The method involves mapping and affine transformation to obtain an ideal grasping area space for the target goods, using information gain to explore the perspective of the ideal grasping area space, and obtaining the next optimal perspective for dynamic grasping planning of the intelligent logistics robot. Weights are normalized and relaxed for training the candidate operation set, constructing a neural network topology search architecture space, and using a differentiable structure to search the neural network topology search architecture space to generate a grasping convolutional neural network. This invention enables precise picking control of target goods identification, detection, labeling, and grasping by intelligent logistics robots, thereby improving the picking performance and accuracy of intelligent logistics robots.

The application discloses a neural network topology structure mapping method for a many-core architecture, uses a four-step algorithm framework based on scale reduction, preliminary segmentation, scale expansion and mapping scheme construction, saves the topology structure to a file system, and applies a graph partition algorithm and a force guiding algorithm, so that memory occupation during compilation of the topology structure of a large-scale neural network is greatly reduced, and the range of the neural network that can be deployed to a neural computing chip is expanded. Meanwhile, by using a heuristic algorithm specific to a mapping problem, the number of iterations and running time are greatly reduced, the compilation efficiency is improved, and the quality of the compilation result is ensured.

Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes calculating one or more connection constraints based on analog integrated circuit (IC) design constraints. The method also includes transforming the neural network topology to an equivalent sparsely connected network of analog components satisfying the one or more connection constraints. The method also includes computing a weight matrix for the equivalent sparsely connected network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent sparsely connected network.

Systems and methods herein disclose technology, architectures) and constructs of artificial (machine) brains having the feature of separated hardware and software wherein their full functionality is defined by software only. Implementations not only replicate all known functions of a human brain including consciousness but also establish universal, novel construction and characteristics of software-defined brains realizing any possible, known or yet unknown functionality of an intelligent computer. Additional aspects disclose classes of different Neural Network (NN) topologies, based on the universal representation of a software-defined nerons an d / or software-defined neural networks to implement any kind of known or yet unkno w n neural network via software implementations embodied on any universal hardware, which enable subjective and objective phenomena utilized for processing machine consciousness and machine subconsciousness. Various illustrative functional complexes are set forth via the disclosure of a. new class of neural networks, namely the Heterogeneous Hierarchical Model (HHM).