114 results about "Neuronal models" patented technology

The invention is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention discloses a pulse neural network image classification method and system based on a space-time adaptive threshold, mainly solving the problems of poor nonlinear expression and limited time sequence and space feature processing ability in the prior art, and the scheme comprises the following steps: obtaining an image data set, and dividing the image data set into a training set and a test set; a spiking neural network main body structure comprising an input layer, a hidden layer and an output layer is selected, an existing neuron model is improved by introducing a space-time joint threshold adjustment mechanism, and improved neurons are placed in each neuron layer in the hidden layer to form a spiking neural network based on a space-time adaptive threshold. The training set is used to carry out iterative training; and inputting the test set into the trained pulse neural network to obtain an image classification result. According to the method, a space-time adaptive threshold mechanism is introduced, the threshold can be dynamically adjusted to adapt to time and space features, the processing capacity of the network on time sequence data and complex features and the classification accuracy of images are remarkably improved, and the method can be widely applied to dynamic visual tasks and event-driven scenes.

The invention belongs to the technical field of image encryption, and discloses a medical image compression and encryption method and system based on a memristor FHN neuron model, and the method comprises the steps: fusing n color or gray medical images of any size into an M * N * 3 fusion image; sparse representation is carried out on the fused medical image in combination with discrete wavelet transform, and redundant data are reduced; initial conditions and control parameters of an m-FHN chaotic system are generated through an SHA-512 hash value of an original image, and strong association of a secret key and a plaintext is ensured; the m-FHN system is iterated to generate three groups of chaos sequences, and the chaos sequences are quantized to obtain a pseudo-random matrix for diffusion and scrambling operation; performing forward diffusion, index scrambling and backward diffusion operation on the sparse image in sequence to obtain a final compressed and encrypted image; a receiving end acquires an initial parameter and a control parameter bound with a plaintext through a secure channel, and then performs inverse backward diffusion, inverse index scrambling, inverse forward diffusion and inverse discrete wavelet transform on a ciphertext image in sequence to obtain a decrypted image.

The invention discloses a self-adaptive frequency collaborative support method and system for an energy storage and new energy grid-connected device under a weak power grid, and the method comprises the steps: firstly determining the expandable active power, locking the frequency of a grid-connected point of the weak power grid, and inputting the frequency into a neuron model; and then constructing a fuzzy rule reasoning table, defuzzifying to obtain an adaptive neuron proportionality coefficient, determining an adaptive correction amount of a frequency reference value, and calculating a reference value of total active power. Safe operation and equipment protection of the system are considered at the same time, the power adjustment range and the power change rate of the unit are limited, an economic-frequency collaborative optimization objective function is established, a power reference value is obtained through calculation, and then a power instruction is issued; and finally, monitoring the actual output power of the station, and giving out early warning when the deviation exceeds a threshold value. According to the method, the correction amount of the frequency reference value is determined from the perspective of eliminating the frequency steady-state error of the weak power grid, the active power is distributed from the perspective of collaborative support, and the collaboration, economy and reliability of the output of the energy storage device and the new energy station are realized.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention relates to the technical field of new energy system fault diagnosis, in particular to a solar cell coupling hydrogen production system fault diagnosis method based on big data, and the method comprises the steps: collecting photovoltaic, electrolytic cell and control unit multi-source heterogeneous data, and detecting the abnormality through an abnormality detection core model; matching and repairing by combining historical similar sections to obtain operation re-repairing data; a multi-scale expansion convolutional neural network is adopted to extract time sequence fault features, a backbone network is adopted to extract infrared image high-order features, and heterogeneous aggregation features are obtained through fusion of a spiking neuron model and virtual dendritic branches; constructing dynamic graph nodes and attributes according to features and components, and integrating multiple indexes to fit a causal association strength weight; and multi-link diagnosis: obtaining a fault type through a quantum annealing algorithm, positioning a fault through causal discovery and a virtual dry prediction algorithm, integrating and reporting to a server. The method can improve the fault diagnosis accuracy and real-time performance, and is suitable for fault monitoring of the solar hydrogen production system.

There is provided an improved neuron model for spiking neural networks. The neuron model comprises somatic synapses as well as dendritic synapses. The somatic synapses are used for receiving an input token, and the dendritic synapses are used for receiving contextual data. The contextual data is used to modulate the behavior of the spiking neuron. The neuron model may be used to implement an attention mechanism in SSNs and to implement a Mixture-of-Experts architecture.

The invention provides an energy storage and fire fighting intelligent linkage method and system based on hybrid control, and the method comprises the steps: carrying out the collection and standardization processing of an input multi-source data signal through a preset sampling frequency, and converting the processed data signal into a pulse sequence; an improved LIF neuron model is adopted to construct a three-layer architecture network, and the three-layer architecture network is utilized to perform characteristic extraction and decision generation on an input pulse sequence; and decoding the pulse sequence output by the three-layer architecture network, mapping a decoding result into a specific linkage control instruction, and executing linkage control operation according to the linkage control instruction. The method has the advantages that multi-source data fusion and self-adaptive control can be well achieved, the use requirements of various complex and changeable application scenes are met, meanwhile, the real-time performance and reliability of the whole system can be guaranteed, dynamic extension and decision optimization are supported, and therefore many defects existing in a traditional predefined rule scheme can be well overcome.

The invention discloses a multi-frequency weak fault enhancement method based on fractional order resonance response atlas reconstruction, and the method comprises the steps: carrying out the noise reduction, trend term removal, phase compensation and modal decomposition of a mechanical vibration signal, determining a lag phase through a frequency sweep method for phase compensation, adjusting the phase of a same-frequency signal, reconstructing an effective component through the combination of a modal decomposition borrowing geometric method and a Pearson matrix, and carrying out the phase compensation. Constructing a nonlinear resonance model with historical memory; inputting the parameters into the same-direction coupling neuron model, dynamically updating and simulating transmission intensity change through synaptic weight, and solving response and step length; constructing a zero-energy coupling index based on a signal zero-crossing behavior, energy distribution and time-frequency analysis, wherein the time-frequency analysis comprises short-time Fourier transform and wavelet packet energy spectrum calculation to extract energy features; and iteratively optimizing parameters by using a quantum optimization algorithm, adjusting a rotation angle and preserving population diversity to prevent premature convergence, and outputting optimal parameters by taking a zero-energy coupling index as a target.

The invention discloses a pulse graph neural network space-time signal detection method and device, a medium and a product, and belongs to the technical field of space-time signal detection. According to the method, graph structure data of a system network is processed through a trained multi-source information fusion pulse graph neural network model, and the multi-source information fusion pulse graph neural network model comprises a graph convolutional neural network and a pulse graph neural network. The pulse graph neural network comprises an input layer, a self-adaptive mixed space-time pulse neuron model layer, an attention layer, a full connection layer and an output layer which are connected in sequence, and the advantages of the graph convolutional neural network and the pulse neural network are combined, so that efficient detection of the dynamic space-time signals of the complex topology network is realized; and the real-time performance and the accuracy of signal processing are improved.

The invention relates to the technical field of pulse neural network computing hardware, in particular to a neural network computing circuit of a pulse self-attention mechanism. According to the method, invalid or inefficient pulse events are dynamically screened through the hardware mask module, the operation number is remarkably reduced, and calculation path delay and logic resource occupation are reduced; meanwhile, in order to adapt to novel networks with binary architecture such as QKFormer, calculation and storage of a V matrix are eliminated, and storage resources and calculation resources are further reduced; in addition, the event coding module only generates active neuron events, and input sparsity is achieved. A configurable IF neuron model is adopted, exponential operation is avoided, hardware implementation is facilitated, and the method is suitable for binary network deployment. The modular architecture can support function extension, assembly line and parallel work; and the parallelism degree of the design can be determined according to the actual data pulse distribution rate. Event driving and mask pruning are combined, so that the overall computing resources of the circuit are greatly reduced, and the power consumption is reduced.

This invention discloses an olfactory electrostimulation model for automatically generating current stimulation signals for an artificial nose, relating to the technical field of olfactory stimulation signal generation using biomimetic neural networks. The implementation steps include: First, constructing a biomimetic neural network framework. Based on the neuronal anatomical structure of the olfactory epithelium and olfactory bulb system, extracting the main neuron types, and organizing them according to their connection methods in biological systems; Second, using the Hindmarsh-Rose neuron model to model various neurons in the constructed network and simulate their dynamic firing behavior; Subsequently, setting the connection weights between neurons and the interlayer transmission current to realize the computational model of the entire biomimetic neural network; Finally, in the odor encoding and stimulation signal generation stage, the odor detection curve of the electronic nose is used as an external stimulus input to the biomimetic neural network. The network calculates and generates a series of peak signals, which are output as current stimulation signals to the olfactory bulb for stimulation, thereby realizing the olfactory induction function of the artificial nose.

This invention discloses a method for constructing a spiking neural network based on recurrent firing neurons, comprising: S1: constructing a recurrent firing spiking neuron model; S2: calculating positive and negative pulses based on the gradient function corresponding to each time step in the recurrent firing spiking neuron model to obtain cross-time-step positive and negative pulses; S3: backpropagating the cross-time-step positive and negative pulses back through the recurrent firing spiking neuron model to obtain a spiking neural network; S4: training the spiking neural network using a positive and negative membrane potential balance loss function to obtain a recurrent firing spiking neural network. This invention significantly improves the learning ability and information representation ability of the spiking neural network, while enhancing its robustness.

The application discloses a Spike-BP on-chip learning method and system based on a Ca-LIF neuron model and a processor, wherein a trainable linear leakage parameter and a positive and negative double-channel pulse transmitter mechanism based on calcium gating are adopted; for the Ca-LIF neuron model, a linear leakage operation can be realized only by subtraction, complex leakage compensation operations can be avoided when a gradient matrix is solved, hardware implementation complexity is reduced, a large amount of calculation resources of subsequent hardware design is saved, and the performance of pulse neural network training is improved.

The application discloses a three-dimensional Hindmarsh-Rose neuron model circuit under periodic excitation, comprising three neuron circuits and a matching circuit, and is composed of nine operational amplifiers and two multiplier circuits. The three-dimensional Hindmarsh-Rose neuron model circuit under periodic excitation can provide hardware support for studying neuron dynamic behaviors under periodic excitation, and has important theoretical research significance and practical application value.

The present invention relates to a method for enhancing the fault symptom of a dual-target optimized co-directional linear coupled biological neuron. A co-directional linear coupled biological neuron module is constructed by a co-directional linear coupled hyperbolic tangent biological neuron and a Gaussian biological neuron. Then, a characteristic frequency amplitude ratio measurement index and a dwell time distribution index are designed based on the model response as the dual-target synchronous optimization function of the genetic algorithm to optimize the parameters of the co-directional linear coupled biological neuron model, realizing the extraction of weak symptom features of strong noise and strong coupling machinery under complex working conditions and timely diagnosing the early weak faults of mechanical equipment.

This application discloses a method, apparatus, electronic device, and storage medium for recognizing facial expression images, relating to the field of image recognition technology. It includes: determining the original vector of the expression image based on its pixel values; performing principal component analysis on the original vector to obtain the target vector of the expression image; inputting the target vector into a feature extraction network to obtain feature information of the expression image; encoding the feature information using pulse frequency based on a Poisson distribution to obtain a pulse sequence of the expression image; and inputting the pulse sequence into an expression recognition model to obtain the recognition result of the expression image. The expression recognition model is a multilayer spiking neural network, where the spiking neuron model is an improvement based on the leaky integral discharge neuron (LIF) model. This application's technical solution offers better interpretability and flexibility, significantly reducing network energy consumption while improving computational power, making it suitable for more intelligent small robots such as customer service robots.

The invention discloses a novel discrete memristor coupling heterogeneous neuron model construction method and application, and designs a novel discrete memristor model to simulate two different neuron models of synaptic coupling to obtain a final novel discrete memristor coupling heterogeneous neuron model. A phase diagram, an iterative sequence, a bifurcation diagram, a Lyapunov exponential spectrum and other methods are used for analyzing the dynamic characteristics of the model, and it is found that the model presents rich dynamic behaviors such as hyper-chaos, chaos and periods and a neuron discharge mode. The high complexity shows that the novel discrete memristor coupling heterogeneous neuron model can be applied to the security field of image encryption and the like. The constructed coupled neuron model is a discrete system, so that the digital circuit of the model is easy to realize through a DSP (Digital Signal Processor). The coupling neuron model-based application to image encryption is good in effect and high in security.

The invention discloses a low-power-consumption buffer circuit for neuron stimulation, which belongs to the technical field of microelectronics and comprises a neuron model, a current driving module, a buffer stage operational amplifier and a feedback resistance network. The neuron model is used for simulating electrical characteristics of biological neurons, the input end of the neuron model is connected with a node WE of the current driving module, and the output end is connected with a node RE; the current driving module is used for generating a current stimulation pulse under the control of the enable signal and providing the current stimulation pulse to the input end of the neuron model for electrical stimulation; the buffer stage operational amplifier is used for stabilizing the port voltage of the neuron model at a node RE, and comprises an amplification stage, a bias stage, a current buffer stage, an output stage and an auxiliary current stimulation stage; and the feedback resistance network is used for sampling voltage from a node RE and providing feedback voltage for the buffer stage operational amplifier after voltage division. The high-performance buffer circuit under low power consumption is realized, the standby time is prolonged, the chip area cost is saved, and integration is facilitated.

The application discloses a simulation system supporting fast large-scale brain simulation, and belongs to the technical field of brain simulation.The simulation system comprises a hardware device layer, which provides a plurality of hardware device resources; a data communication layer, which provides a plurality of types of communication modes between the computing nodes; an operation abstraction layer, which provides a plurality of types of neuron models, synapse models, connection rules and learning rules; an API layer, which provides an API interface and receives user requirements through the API interface; a hardware abstraction layer, which calls corresponding hardware kernels; and a network abstraction layer, which firstly records the topological structure of a brain simulation network, performs resource allocation and neuron cluster mapping, then creates the brain simulation network on the hardware device, and finally performs training or execution of the brain simulation network.The application fully utilizes cluster hardware resources to realize faster and larger-scale brain simulation, and solves the problems of lack of resource scheduling and resource allocation, insufficient utilization of device parallelism, too long communication time consumption, and limited support of hardware and interfaces.

The invention relates to the field of gas concentration detection and intelligent traceability, in particular to a gas traceability trajectory decision-making method based on a pulse neural network, and aims to solve the problems of low gas source traceability precision, slow response and poor adaptability in the prior art. According to the method, a plurality of gas sensors and wind direction sensors are arranged, gas concentration and wind direction data are collected in real time, pulse coding processing is adopted, and the data are converted into pulse signals to be input into a pulse neural network. The spiking neural network adopts a leakage integral and distribution neuron model, and can dynamically adjust the moving direction of the equipment according to the change of gas concentration and wind direction data. By training the neural network, the network can learn and make decisions in real time, and the equipment is controlled to accurately track the gas source along the gas concentration gradient. According to the method, through multi-sensor data fusion and real-time learning, the unmanned equipment can quickly adapt to and accurately execute a gas source tracing task in a complex environment, and the precision and efficiency of gas tracing are remarkably improved.

The invention discloses an image encryption method based on fractional order heterogeneous neurons and DNA dynamic coding, and the method comprises the steps: 1) dividing an original color image into a two-dimensional matrix, and carrying out the partitioning processing; 2) constructing a fractional order heterogeneous neuron model; 3) generating a plurality of groups of chaotic sequences and dynamic keys; 4) dynamically selecting a DNA coding rule and an operation rule; 5) continuing to dynamically select the DNA decoding operation rule, and performing DNA decoding on the block matrix; 6) performing replacement operation on the three two-dimensional matrixes; and 7) generating a ciphertext image, combining the two-dimensional matrixes of the three channels after the operation in the steps, and generating an encrypted ciphertext image. The invention further discloses an image decryption method based on the fractional order heterogeneous neurons and DNA dynamic coding. The invention belongs to the technical field of information encryption, and solves the problems of poor security, low processing efficiency and insufficient dynamic adaptability of the existing image encryption technology.

The present application discloses a pulsed convolutional neural network accelerator. After receiving an input image, the controller starts the image sorting unit to sort the image. After the sorting is completed, it starts the PE computing array to perform convolution and pooling calculations or starts the fully connected computing unit to perform fully connected calculations, and caches the results generated by the calculations into the internal image buffer unit. If the current network layer is the output layer, it counts the pulse firing frequency to complete image recognition. If the current network layer is a non-output layer, it starts the image sorting unit; wherein, both the PE computing array and the fully connected computing unit include optimized neurons obtained by using the first-order Euler method to eliminate the differential calculations solved by LIF neurons and Izhikevich neurons, improving the technical problems in the prior art that there is a single neuron model, which cannot support SNNs built by multiple different pulsed neurons, and the hardware resources, computational amount, and power consumption occupied by the computing unit are very large.

The invention discloses a brain simulation-oriented high-performance numerical differential solver method and system. The method comprises the following steps of: receiving a neuron cluster model and simulation parameters thereof; the differential equation definition of the model is analyzed, and when the input is in a character string form, the input is converted into a function form; setting a current moment t, a simulation step length dt and simulation time simt; the simulation parameters are analyzed and classified; judging whether the model contains noise or not, if yes, initializing a stochastic differential equation solver, and otherwise, initializing an ordinary differential equation solver; adaptive operators of different neuron models are called in each step length to calculate numerical solutions, and variables are subjected to parallel calculation and updating in the calculation process; updating the variable yn and the current moment t when tlt; and when t is greater than or equal to simt, entering the next step of length calculation until t is greater than or equal to simt. According to the method, the high-performance differential solver supporting parallel solving of the whole neuron cluster is provided, the parallel acceleration characteristic of hardware such as a GPU is fully utilized, the simulation speed is increased, and large-scale brain simulation is achieved.

The present application provides a spintronic neuron device, a modeling method, and a spiking neural network training method. The spintronic neuron device comprises electrodes at two ends, a transmission component which is located between the two electrodes and formed of multiple layers of films, and multiple output components deployed at different positions of the transmission component. In this way, different output components can be selected to work, to change the length of the transmission path of magnetic domain wall motion, thereby automatically adjusting the firing threshold of the spintronic neuron device. Characteristic simulation behaviors of the spintronic neuron device are modeled, and a spiking neural network for implementing identification tasks is constructed by means of the obtained neuron models having different performance and different mechanisms; the performance of the spiking neural network can be optimized by changing the number of neuron models, the threshold adjustment rate of a firing threshold, and / or synaptic weights, without the need to use peripheral circuits, thereby achieving the homeostasis of the spiking neural network and improving the stability and robustness of the spiking neural network to different inputs.

The invention relates to the technical field of neuron circuits, and provides a configurable neuron circuit and a control method, and the circuit comprises a mode selection module which selects a corresponding target working mode according to a received neuron configuration signal; the calculation module is connected to the mode selection module, and performs corresponding membrane potential updating according to the target working mode selected by the mode selection module and the received pulse information of the presynaptic neurons to obtain a membrane potential updating result; the comparison module is connected to the calculation module and is used for comparing the membrane potential updating result with a preset threshold value and determining whether the current neuron can generate a pulse or not according to a comparison result; and the pulse generation module is connected to the comparison module, and is used for generating a pulse signal and an AER (Advanced Encryption Register) coding group package and outputting updated membrane potential information when the current neuron is determined to generate the pulse. According to the technical scheme, selection of two neuron models can be achieved, the circuit structure is simplified, and meanwhile more complex behavior modes are achieved.

Embodiments of the present application provide a kind of electric appliance fault prediction method and device based on neuron model, electronic equipment and readable storage medium, the method comprises: constructing neuron model and weight regression model, determine the business weight mark and nerve node mark of the neuron model;Obtain the fault diagnosis sample data of specified electric appliance, and according to the fault diagnosis sample data of the specified electric appliance, the business weight mark and the nerve node mark, the neuron model is trained;Using the weight regression model determines the fitting value of the neuron model after training;When the fitting value meets preset threshold value, the neuron model after training is determined as the neuron model of training completion, to carry out fault prediction according to the specified electric appliance of the neuron model of training completion.This embodiment of the present application can avoid the information collected by the electric appliance of fault to cause intelligent perception error, improve the accuracy of intelligent perception.

Embodiments of the present disclosure disclose a text classification method, apparatus, device, and storage medium. The method includes: determining word vectors corresponding to each word in the text to be classified; sequentially inputting the word vectors corresponding to each word into a neuron model of a spiking neural network for processing in the order of the text; when the current word vector is the last word vector in the text to be classified, determining a classification result of the text to be classified according to the output result of the neuron model; wherein the neuron model determines a leakage coefficient according to the output result at the (i-1)-th moment and a target weight matrix, and the leakage coefficient is used to determine the output result of the neuron model at the i-th moment. The technical solution of the embodiments of the present disclosure realizes the classification of long-sequence texts with fewer computing resources.

The embodiment of the invention relates to the field of artificial intelligence, and provides an epilepsy electroencephalogram signal detection method and device based on frequency attention and a pulse recurrent neural network and a storage medium, and the method comprises the steps: obtaining an epilepsy electroencephalogram signal to be detected; performing datum line following coding on the epilepsy electroencephalogram signal based on a grid search algorithm to obtain a coded epilepsy electroencephalogram signal; constructing a bidirectional pulse recurrent neural network model based on a preset adaptive pulse neuron model; the bidirectional pulse recurrent neural network model is used as a reference model, the reference model is adjusted based on a frequency attention mechanism and a pulse full-connection linear coding mechanism, and an improved model is constructed; and inputting the epilepsy electroencephalogram signal into the trained improved model to output a detection result associated with the epilepsy electroencephalogram signal. By adopting the method, the model calculation performance can be improved on the basis of reducing the energy consumption, and the detection effect on the epilepsy electroencephalogram signals is improved.