10 results about "Neural Network Simulation" 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 application discloses an automatic needle insertion method combining a blood flow optimization model with a neural network. The method first calculates the blood vessel position and blood flow velocity distribution of a needle insertion area according to blood flow information measured by automatic ultrasonic scanning by means of the Doppler ultrasonic principle, then establishes a blood flow relative velocity optimization model and a neural network with a selection function, and decides the optimal needle insertion point and needle insertion angle on the basis of physical measurement results, and finally simulates the needle insertion path by means of a neural network with a learning function according to the needle insertion method of a professional. The method can be used for needle insertion of subcutaneous blood vessels of limbs and other body parts, and overcomes the difficulty of manual needle insertion when the blood vessel is not visible to the naked eye, thereby providing technical assistance for self-rescue and first aid in an environment lacking professional personnel.

This application provides a method for preparing a dry binder, achieving stable quality control through dynamic sensing and multi-level intelligent adjustment of the moisture content of the raw material powder: First, moisture content and environmental humidity data are collected in real time, and time series analysis is used to predict the trend of moisture content changes; then, support vector machine is used to classify seasonal and batch differences to determine the main influencing categories; for high humidity interference scenarios, neural network is used to simulate the dynamic impact of material residence time in the mixing equipment on moisture content and determine the risk level; based on the risk level, historical data is retrieved, and a random forest algorithm is used to generate a preliminary adjustment plan for process parameters; then, a digital simulation model is used to simulate and verify the entire process and iteratively optimize it to finally determine the optimal process parameters; during production, the moisture content prediction model is dynamically updated using real-time feedback data to achieve closed-loop correction and continuous optimization. This invention effectively reduces the quality risks caused by environmental fluctuations and batch differences.

This application belongs to the field of delay differential equation modeling, specifically disclosing a delay differential equation modeling method based on Bayesian optimization and neural networks. The method includes: generating multiple trajectory data of the system; searching for the optimal delay term using a Bayesian optimization algorithm, where the delay term to be solved is the optimization variable, and the error based on neural network simulation is the objective function, obtaining the optimal delay term through iterative optimization using a surrogate model; constructing a state matrix and a delay matrix from the trajectory data based on the optimal delay term, using the concatenated matrix as input to a neural network to construct a neural network for approximating a nonlinear function; integrating a linear multi-step method into the loss function of the neural network, training the neural network using the trajectory data to obtain a nonlinear function approximation model; and separating the numerical discretization error and the neural network approximation error based on this model to construct a total error bound. This application can achieve high-precision, high-efficiency delay differential equation modeling with quantization error guarantee.

The application discloses a kind of based on hybrid order memristor hetero neural network simulation circuit, by hybrid order memristor synapse computing module, neuron circuit and cross crosstalk coupling adjustment module composition.Memristor synapse computing module receives system state and membrane potential voltage signal, parallelly executes the dynamic operation of nonlinear memductance weight, neuron circuit and nonlinear crosstalk coupling module carry out membrane potential difference, function activation and synapse current crosstalk summation and continuous integral operation based on Kirchhoff's law, obtain the output result of characterizing neural network multi-mode brain-like discharge.The circuit efficiently realizes the bottom physical mapping of neural network from microcosmic heterosynaptic current crosstalk to macroscopic dynamics evolution.Compared with traditional isomorphic neural network circuit, the application greatly improves the dynamics dimension and biological authenticity of neural network.The application provides a kind of high quasi-state, high parallel hardware solution for brain-like intelligent computing, has originality and important application value in the field of neuromorphic hardware.

The present application relates to the technical field of artificial intelligence decision analysis and behavior modeling, in particular to a kind of situation-reasoning one figure-based character decision mode analysis method and equipment, including the construction decision mode situation-reasoning one figure, the extraction and quantization of decision influencing factors and the extraction and quantization steps of decision influencing factors, in the execution above-mentioned steps, first, the structure of situation-reasoning one figure is used, and massive unstructured open source data is converted into computable, situation-reasoning one figure that can be deduced;Then, according to the decision theory, the internal psychological cognitive characteristics of the character are quantified, and they are used as the key constraint condition of reasoning network;Finally, the decision mode neural network is constructed, and the decision process of "perception stimulation-cognitive screening-logical reasoning" of the character under a certain situation is simulated.The present application realizes the deep integration and accurate analysis from external objective situation to internal subjective cognition.

This application relates to the field of product carbon footprint calculation, and discloses a method and system for constructing a product carbon footprint model based on intelligent agents. This method automatically collects and standardizes product information from multiple data sources using a large language model, generates product production process flows and identifies emission sources through a lifecycle model inference module, simulates activity data using deep neural networks, and matches emission factors in a background database using semantic similarity. Finally, a carbon footprint calculation model is constructed based on the ISO 14067 standard. This method overcomes the problems of difficult data acquisition and time-consuming processes in traditional carbon footprint calculation methods, achieving intelligent construction of product carbon footprint models and improving the efficiency and accuracy of carbon footprint calculation.

This invention discloses an on-orbit intelligent processing method for spaceborne images based on brain-like computing, belonging to the field of on-orbit intelligent processing of spaceborne images. This invention utilizes a spiking neural network to simulate the structure of the human brain: including a feature extraction layer, a pulse coding layer, an STDP learning layer, a lateral inhibition layer, and a decision output layer; and improves model accuracy through on-orbit updates and federated learning. This invention reduces computational energy consumption and improves efficiency. Simultaneously, this invention leverages the synaptic plasticity of neurons to construct a SNN with excellent spatial and temporal resolution.

The application belongs to the technical field of power system operation control and optimal scheduling, and provides a power transmission and distribution reactive power reserve planning method and system considering power distribution network power flow security domain, generates a power flow security domain sample set considering network reconstruction based on an improved radial iterative reconstruction method, uses a physical information graph neural network (PI-GNN) to simulate and solve the security domain in consideration of new energy uncertainty, defines an equivalent four-dimensional security domain cost model of the power distribution network on the basis of considering optimal carbon emission power flow, and deduces and forms a planning scheme of the power transmission and distribution system reactive power reserve considering the operation state under a typical scene. The implementability and economy of the planning scheme under multiple working conditions throughout the year are improved.

The application discloses a kind of based on quick identification's mine filling body spontaneous combustion tendency analysis method, comprising: obtaining mine filling body material field sample data, processing sample data extraction mineral proportion, obtain composition distribution information, adopt neural network simulation different mineral oxidation reaction path, determine interaction intensity value;Based on interaction intensity value, obtain sulfide and organic matter proportion subset, through logical screening rule calibration high-risk interaction combination, obtain risk classification label;Through historical spontaneous combustion data extraction flammable characteristic parameter sequence, from burning characteristic parameter sequence, obtain temperature and gas concentration correlation subsequence, and optimize subsequence, obtain dynamic change trend index;According to dynamic change trend index, judge spontaneous combustion trend type, obtain early warning level label, through early warning level label, obtain mine field environment parameter integration, determine final spontaneous combustion trend prediction result.The application improves the accuracy and real-time of spontaneous combustion risk prediction.