20 results about "Chaotic neural network" patented technology

The invention relates to the technical field of data security, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data encryption method, device, equipment and medium, comprising: acquiring a real-time state vector representing a running state of a chaotic neural network processing unit, monitoring a chaotic characteristic of the real-time state vector to generate a chaotic intensity index, and comparing the chaos intensity index with a preset chaos threshold value, generating an original key based on the real-time state vector when a key updating condition is met, dynamically adjusting a weight matrix of the chaos neural network processing unit according to the chaos intensity index, and performing cryptographic processing on the original key to generate a final key. And performing encryption processing on the target data by using the final key to generate target encrypted data. According to the method, the internal state of the chaotic neural network is used as a dynamic key generation basis, so that the dependence on an external entropy source and fixed parameters is eliminated, and the security and real-time performance of a data encryption process are improved.

The invention provides a medical service item compliance dynamic monitoring method and system based on artificial intelligence, and relates to the technical field of medical service management.The method comprises the steps that a data incidence relation matrix is established through the unbalanced state entropy theory, and risk features are extracted through a chaotic neural network; a risk prediction spectrum is generated in combination with deep generative adversarial diagnosis and treatment modeling and heterogeneous medical network neural evolution analysis, and a causal reasoning decision tree is adopted to construct a time sequence evolution model to position abnormal risk points. The medical service project compliance real-time monitoring and risk early warning can be realized, the supervision efficiency is improved, and the medical risk is reduced.

The invention relates to the technical field of engineering construction, and provides an underground powerhouse surrounding rock stability prediction method, system, device and medium. Performing fractal dimension modeling on the time sequence monitoring data to obtain a detection fractal dimension function, and performing local search on a scaling factor, a correction coefficient and a scale factor in the detection fractal dimension function according to a hybrid leapfrog algorithm to obtain an optimal scaling factor, an optimal correction coefficient and an optimal scale factor; based on the optimal scaling factor, the optimal correction coefficient, the optimal scale factor and the detection fractal dimension function, constructing a target dimension feature extraction function, and extracting a target dimension feature value in the time sequence monitoring data according to the target dimension feature extraction function; and inputting the target dimension characteristic value and the surrounding rock sampling vector into a chaos neural network for surrounding rock deformation prediction, and obtaining a surrounding rock deformation prediction result. According to the invention, the timeliness and accuracy of surrounding rock stability monitoring and early warning can be improved.

The invention discloses a rail transit operation and maintenance decision analysis method based on a big data processing technology, and the method comprises the steps: collecting multi-source data through the deployment of a bionic cilium sensor array and metasurface noise reduction, mining fault features through a chaotic neural network, and carrying out the early warning; constructing a dynamic heterogeneous knowledge graph, and realizing rapid reasoning by using a bionic vision mechanism; an operation and maintenance decision considering multiple targets is generated based on ecological game optimization, and digital twinborn verification is passed; the method further comprises the steps of cross-dimension data fusion traceability, edge intelligent collaboration, knowledge dynamic updating and the like, and intelligentization and high efficiency of rail transit operation and maintenance are achieved. According to the method, fault early warning is advanced to 96 hours, the operation and maintenance cost is reduced, the energy consumption is reduced, the knowledge updating accuracy is high, and the reliability, economy and safety of rail transit operation and maintenance are improved.

The invention discloses an automatic defense system for computer network security, and the system comprises a quantum random walk edge real-time detection module which is responsible for capturing a data flow in a network in real time, and detecting the abnormal transition in the data flow through a quantum random walk model; the hyper-chaos time sequence prediction module is used for performing time sequence prediction on the data flow by utilizing a hyper-chaos neural network, identifying potential security threats, quantifying the chaos degree of the system through a Lyapunov index, dynamically setting an abnormal threshold value, and sending out an early warning signal when a prediction result exceeds the threshold value; the quantum key distribution cross-system collaboration module is used for encrypting threat intelligence exchange between external systems by using a quantum key distribution technology, and meanwhile, through an intelligent contract automation mechanism, when specific conditions are met, a whole-network defense action is automatically triggered; and the dynamic causal reasoning real-time feedback module is used for constructing a processor-network-application causal Bayesian network, and quickly positioning and blocking a threat source by deducing an attack propagation path in real time.

The invention discloses a multi-modal industrial defect detection method based on a chaotic neural network, and the method comprises the steps: obtaining and aligning industrial defect data of multiple modals, carrying out the feature extraction and fusion through a multi-branch neural network, and generating a unified defect feature vector; taking the feature vector as an initial condition of a preset high-dimensional chaotic system, and generating a binary hash code which is extremely sensitive to the initial condition through iterative operation; in a pre-constructed defect instance library in which historical defect hash codes and associated production data thereof are stored, rapid retrieval is performed by using the hash codes to obtain historical instances similar to the current defect, and the associated production data thereof are aggregated to perform automatic root cause analysis; according to the method, the sensitivity of a chaotic system to an initial value is utilized to amplify feature tiny differences, so that the precision and efficiency of nonlinear feature retrieval are remarkably improved, seamless integration from efficient retrieval to intelligent root cause analysis is realized, and a data closed loop from defect detection to process optimization is formed.

The application belongs to the technical field of image encryption and discloses a discrete fractional-order neural network chaotic image encryption system and method based on RNA dynamic transcription and DNA coding, which comprises a model construction module, a key generation module and an image encryption module; the model construction module is used for constructing a fractional-order neural network and making the encryption system in a hyperchaotic state based on the fractional-order neural network; the key generation module generates an RNA key stream through three rounds of dynamic transcription with a seed sequence as a starting point; the image encryption module encrypts an image through triple hybrid diffusion and then diffuses the image again based on the RNA key stream to obtain an encrypted image; the application realizes deep confusion and diffusion of image pixels by constructing a high-complexity fractional-order chaotic neural network and combining an RNA+DNA dual biological coding mechanism, thereby improving the security, randomness and robustness of the encryption system.

The present invention discloses a color image encryption method, system and device based on a chaotic neural network, including extracting the R component, G component and B component of a color image, respectively generating an R component scrambling transformation key, a G component scrambling transformation key and a B component scrambling transformation key based on a three-dimensional chaotic neural network, performing row and column random scrambling on the color image according to the scrambling transformation key to obtain a scrambled image, splitting the scrambled image into multiple parts by columns, respectively generating diffusion keys based on the three-dimensional chaotic neural network, diffusing all parts of the split image according to the diffusion keys to obtain all parts of the diffused image, scrambling and splicing all parts of the diffused image by using an array splicing function to obtain a spliced image, synthesizing the components of the spliced image to obtain an encrypted image of the color image, strengthening randomness, enhancing the anti-attack ability and improving security.

The application provides an OFDM encrypted signal PAPR suppression method and system based on a self-encoder, which comprises the following steps: collecting a data sequence of an OFDM system, splitting a complex signal of the data sequence into a real part and an imaginary part to obtain an input sequence; inputting a chaotic neural network; allowing the chaotic neural network to perform chaotic encryption on the input sequence to obtain encrypted data; inputting the encrypted data into an encoder, allowing the encoder to extract features of the encrypted data and compress the features to a low latitude to obtain a low latitude result; inputting the low latitude result into a decoder to obtain a reconstruction result; performing inverse fast Fourier transform on the reconstruction result to obtain a time domain signal; converting the time domain signal into a frequency domain form for reconstruction to recover an original signal; and restoring the data sequence according to the original signal. The application has a significant improvement in reducing the performance of an encrypted OFDM signal, and the improvement is 10.7 dB compared with a non-suppression algorithm and 2.1 dB compared with an existing PRNet scheme.

The invention relates to the technical field of communication encryption, and particularly provides an encryption method which comprises the following steps: generating a key sequence required by an encryption system based on a deep hyper-chaos neural network, the key sequence comprising a key # imgabs0 #; the life game model generates a key base sequence based on evolution rules and iteration times determined by # imgabs1 # and # imgabs2 #; encoding a bit stream signal into a signal base sequence on the basis of a DNA encoding rule determined by # imgabs3 #, and encoding a key base sequence into a diffusion base sequence on the basis of a DNA encoding rule determined by # imgabs4 #; performing DNA calculation on the signal base sequence and the diffusion base sequence based on a DNA operation rule determined by # imgabs5 # to obtain an encrypted base sequence; based on a DNA decoding rule determined by # imgabs6 #, the encrypted base sequence is converted into an encrypted binary bit sequence which can be transmitted in an actual channel; the method has a larger key space, and the capability of resisting brute force cracking is higher.

The present invention provides a method and system for dynamically monitoring the compliance of medical service items based on artificial intelligence, which relates to the technical field of medical service management. The method includes establishing a data correlation relationship matrix by using the non-equilibrium entropy theory, extracting risk features through a chaotic neural network, combining deep generative adversarial diagnosis and treatment modeling and heterogeneous medical network neural evolution analysis to generate a risk prediction spectrum, and constructing a time-series evolution model by using a causal inference decision tree to locate abnormal risk points. The present invention can realize the real-time monitoring and risk warning of the compliance of medical service items, improve the supervision efficiency, and reduce the medical risk.

The invention relates to the field of mechanical optimization design, in particular to a mechanical optimization design method based on a four-dimensional chaotic neural network. According to the method, electromagnetic radiation simulated by the memristor acts on the designed novel Hopfield neural network, and a chaotic sequence is introduced into a mechanical optimization design process by utilizing rich dynamic chaotic phenomena of the memristor neural network; and continuously substituting the chaos sequence into a constraint equation, circularly judging whether a constraint condition is met or not, and continuously iterating minimum output to obtain an optimal solution. According to the method, the optimal solution of the mechanical optimization design can be quickly and effectively solved, and the Hopfield neural network is used for solving the mechanical optimization design problem and even other optimal design problems in the future, so that the method has a good application prospect.

The invention relates to the field of natural language processing, and discloses a language semantic analysis method based on a self-adaptive chaos neural network, which comprises the following steps: establishing Logistic mapping, and updating the neuron state of the neural network; in the semantic analysis task, analyzing the characteristics and distribution of the language data; cleaning the input natural language text, and removing noise characters; and analyzing the extracted semantic features to obtain final semantic understanding, and generating an interpretation text. The system is excellent in semantic analysis accuracy, adaptability and efficiency, complex semantics can be accurately analyzed, the system is widely adapted to various texts, the real-time requirement is efficiently met, and powerful power is injected for progress of the natural language processing technology.

The application relates to the technical field of signal processing, in particular to a chaotic neural network with complex-valued weights and application of the chaotic neural network in electrocardiogram classification. First, a complex Logistic chaotic mapping with complex-valued parameters is proposed, and bifurcation diagrams, Lyapunov exponents and chaotic attractors of the complex Logistic chaotic mapping are analyzed. Second, the ergodicity of CLCM and a new neuron function are used to optimize the weights of the CNN. Then, the method is verified by using the MIT-BIH database. Through band-pass filtering and double-threshold processing, the electrocardiogram signal is processed into a signal with a single waveform and a more prominent signal, which is used as the input of the designed CNN. The results show that the accuracy of the CNN with complex weights for electrocardiogram classification is improved. The complex weight chaotic neural network has the ability to prevent the network from falling into a local minimum, improves the recognition accuracy of the electrocardiogram, and has a high recognition accuracy.

This invention relates to the field of image encryption technology and addresses the problems of slow encryption speed and weak resistance to attacks in existing image encryption methods. The image encryption method described in this invention uses a ternary fractional discrete chaotic neural network system to generate a chaotic sequence related to the plaintext. A fractal sorting and scrambling process is then performed on the original image using a fractal model approach. To achieve better results, a double scrambling process is applied to both rows and columns. Finally, a synchronous scrambling and diffusion operation is performed according to the Hilbert curve traversal order, which can simultaneously change the position and size of pixel values. This method is the first to apply fractal thinking to the scrambling process in encryption, achieving good scrambling results. The synchronous scrambling and diffusion operation further improves the encryption efficiency to a certain extent.

The invention discloses a medical image content extraction method based on a trainable multilayer RBF-Chaotic neural network. According to the method, medical image content is extracted based on the trainable multilayer RBF-Chaotic neural network; the trainable multi-layer RBF-Chaotic neural network is obtained by simplifying a LeNet-5 network structure, and comprises the steps of removing all maximum pooling layers in the LeNet-5 network structure, reconstructing a classifier into a three-layer full-connection layer, and introducing chaotic momentum generated by a 2DES function in a training process to replace traditional SGD momentum. According to the method, the model is thinner, the occupation of a video memory is lower, a consumer-level video card can be completely loaded, offline clustering is not needed, the model only changes training and does not change reasoning, hospital night long-time training can be successful at a time, and the manual parameter adjustment cost is remarkably reduced.

The application belongs to the field of memristor neural network and secret communication, and particularly relates to a global projection synchronization method of a four-element value memristor neural network based on open-loop control and application thereof. The method is different from the traditional separation of the system into a real number system and three complex number systems, but the system is regarded as a whole for research. Meanwhile, a discrete multi-time delay memristor neural network is used to establish a corresponding drive system and response system, and an open-loop controller is designed, so that the transmission of the encrypted plaintext signal in the channel can achieve better security effect. Compared with the traditional chaotic neural network, the application has better security effect, lower network energy consumption, and provides a better solution for encrypted communication.

The present invention relates to an air-deformation device for wool-like air-textured yarn and a preparation process thereof, belonging to the field of textile technology. The air-deformation device improves the uniformity of the air-deformed yarn through a two-stage air-deformation structure. The arc-shaped trumpet-shaped yarn outlet reduces occasional unevenness caused by the yarn colliding with sharp cross-sections. The new tile-shaped baffle has a certain degree of freedom and slight vibration. Incomplete loops can be further fluffed here, making the yarn surface loop distribution more uniform. It also effectively reduces the surface loop collapse caused by the yarn contacting the baffle body and bending, reducing the loss of wool-like effect. At the same time, it can also reduce the strength loss caused by the high-speed airflow carrying the yarn to impact the baffle body. In terms of the preparation process, it combines advanced chaotic neural network optimization technology and achieves a high degree of automation and intelligence in the preparation process by accurately optimizing and controlling multiple process parameters such as the original yarn characteristics, overfeed rate, and air intake parameters.

The invention belongs to the technical field of image encryption, and discloses a discrete fractional order neural network chaotic image encryption system and method based on RNA dynamic transcription and DNA coding, and the system comprises a model construction module, a key generation module and an image encryption module. The model construction module is used for constructing a fractional order neural network and enabling the encryption system to be in a hyper-chaos state based on the fractional order neural network; the key generation module takes the seed sequence as a starting point, and generates an RNA key stream through three rounds of dynamic transcription; and the image encryption module performs triple hybrid diffusion encryption on the image, and then performs diffusion again based on the RNA key stream to obtain an encrypted image. By constructing the high-complexity fractional order chaos neural network and combining an RNA + DNA dual biological coding mechanism, deep confusion and diffusion of image pixels are realized, and the security, randomness and robustness of an encryption system are improved.

The invention discloses an astrocyte memristive network driven time sequence analysis and dynamic security method, system and device and a medium, and belongs to the technical field of computer vision and information security, and the method comprises the steps: collecting a video stream, and extracting a human skeleton sequence; performing space-time alignment evaluation on the skeleton sequence and a standard template, and identifying an abnormal action; constructing a quadruple synaptic network comprising Chay neurons, HR neurons, astrocytes and memristors, and dynamically adjusting electrical coupling parameters through gesture recognition to generate chaotic signals; extracting multi-dimensional features from the chaotic signal, and generating a key stream through Hash enhancement and key expansion; encrypting the video data by using the key stream; and generating a scoring suggestion. Lightweight real-time encryption is realized through the biologically inspired chaotic neural network, the problem of high calculation complexity of a traditional encryption algorithm is solved, real-time performance and safety are both considered, gesture dynamic control is supported, and the method is suitable for scenes such as rehabilitation training and posture correction.