57results about How to "Improve self-learning ability" patented technology

The invention discloses an intelligent fault diagnosis system for an ICNI system, which can improve the maintenance efficiency, carry out intelligent and automatic diagnosis and is applicable to the ICNI system. According to the technical scheme of the invention, a knowledge base and a management module thereof carry out standardization research and mathematical modeling based on a fault tree, an SQL Server database software framework is adopted, a relational database is used for building a logic relation among a fault phenomenon, a fault mode, a detection method, a historical case and a fault tree internal event to form the knowledge base; and a diagnosis information acquisition module interacts with an automatic testing system via Ethernet to acquire diagnosis data from the ICNI system and a testing instrument, a reasoning machine module adopts CBR and RBR hybrid diagnostic reasoning, after comprehensive judgment is carried out on the fault phenomenon inputted by the user, the field knowledge stored by the knowledge base and the diagnosis data from the automatic testing system, a reasoning method is automatically selected to carry out reasoning diagnosis on the fault, a reasoning process and a reasoning result are outputted to an explanation machine module, and a diagnosis report is generated.

The invention discloses a robust controller of a permanent magnet synchronous motor based on a fuzzy-neural network generalized inverse and a construction method thereof. The construction method of the invention comprises the following steps of: combining an internal model controller and a fuzzy-neural network generalized inverse to form a compound controlled object; serially connecting two linear transfer functions and one integrator with the fuzzy-neural network with determined parameters and weight coefficients to form the fuzzy-neural network generalized inverse, serially connecting the fuzzy-neural network generalized inverse and the compound controlled object to form a generalized pseudo-linear system, linearizing a PMSM (permanent magnet synchronous motor), and decoupling and equalizing the linearized PMSM into a second-order speed pseudo-linear subsystem and a first-order current pseudo-linear subsystem; and respectively introducing an internal-model control method in the two pseudo-linear subsystems to construct the internal model controller. The robust controller of the invention has the advantages of overcoming the dependence and local convergence of the optimal gradient method on initial values and solving the problems of randomness and probability caused by using the simple genetic algorithm, obtaining the high performance control, anti-disturbance performance and adaptability of the motor and simplifying the control difficulty, along with simple structure and high system robustness.

The invention discloses an overlay convolutional network-based rolling bearing failure mode recognition method and device, and relates to the field of rolling bearing failure diagnosis. The method comprises the following steps of: extracting a time-frequency domain feature of a vibration signal of a state-known rolling bearing; normalizing the obtained time-frequency domain feature of the state-known rolling bearing into a feature pixel according to a CNN network input format; inputting the feature pixel into a CNN network, and adjusting a model parameter of the CNN network through carrying out forward self-learning and gradient descent-based counter-propagation on the CNN network so as to obtain a trained CNN network; and during the recognition of a practical rolling bearing failure mode, extracting high-order features capable of reflecting intrinsic information layer by layer by utilizing the trained CNN network by taking a time-frequency domain feature of a vibration signal of a state-unknown rolling bearing, and inputting results of the feature self-learning into a top classifier layer by layer, so as to realize failure mode recognition of the rolling bearings under multiple working conditions and strong noises.

The invention relates to an environment quality detection control system and method. The system comprises an environment acquisition unit, a voice recognition unit, a network access unit, a data analysis unit and an environment quality adjustment unit. The environment acquisition unit carries out environment data acquisition and generates environment data. The voice recognition unit recognizes human voice, generates voice data and carries out simulation voice playback. The network access unit accesses the Internet, and acquires meteorological data and air quality data on the Internet, and uploads the voice data and corresponding command and environment data to the Internet cloud for storage. The data analysis unit carries out comprehensive analysis on various data and generates control data and voice feedback data. The environment quality adjustment unit adjusts the quality of indoor environment. According to the invention, environment quality detection, environment quality control, network access and voice recognition can be carried out; a user interacts with a device through a language; and the user can easily understand the outdoor environment quality for trend prediction and environment data statistics.

The invention provides an online advertisement recommending system and method for large-scale medium data and relates to the technical field of the calculation advertisement science. An advertisement dispatch engine module in the online advertisement recommending system is respectively connected with a user side, an advertisement management module and a flow analysis module. Parameter exchange is carried out between the flow analysis module and an advertisement searching module, a user behavior inquiry module and a webpage management module. A user behavior mining module is respectively connected with the advertisement management module and the user behavior inquiry module. The advertisement management module is connected with the advertisement searching module. According to the online advertisement recommending method, when a user finish accessing a webpage, the user is identified according to user information, user interests are inquired, user behaviors are learned, matched advertisements are searched for according to the predicted user behaviors, and finally the online advertisements are recommended to the user. The system has the good self-learning ability, can effectively improve the intelligent level of advertisement recommendation, and is suitable for online advertisement recommendation under the background with the large-scale data.

This invention discloses one network intruding testing method and to one safety test method and to the test method to judge whether the network data flow to be tested is intruding, wherein the method comprises the following steps: pre-processing; studying step, testing step data process structure, wherein, the studying and testing steps adopts BP network as module for data processing and for studying or testing steps according to different system status.

The invention belongs to the field of control of an electro-hydraulic servo system, and relates to a force/position cooperative control method of an electro-hydraulic servo system. In the implementing process of the method, a position output signal and a force output signal of a valve control cylinder of the electro-hydraulic servo system in a work process are analyzed, outer-loop control of force is additionally provided as feedforward compensation based on position control, a PID controller and an adaptive fuzzy neural network controller are designed to respectively and individually control a position control portion and a force control portion, and cooperative control of the position signal and the force signal of the electro-hydraulic servo system is finally realized. The object of the invention is to reduce the vibration and the impact in the work process of the electro-hydraulic servo system due to stress and improve the positioning precision and stability of the system. The method includes steps: the position control portion measures the position output signal of the valve control cylinder through a displacement sensor and feeds back the position output signal to a position signal input portion, the position output signal is compared with an input signal, and a position error signal is obtained; the force control portion measures the force output signal of the valve control cylinder through a force transducer and feeds back the force output signal to a force input portion, the force output signal is compared with a force input signal, and a corresponding force error signal is obtained; and finally the error signal of the position control portion and the error signal of the force control portion are added (namely the force error signal is regarded as feedforward compensation) as a position expected input error signal of the whole valve control cylinder, the valve control cylinder dynamically adjusts the position signal and the force signal of the valve control cylinder by employing incremental control, and cooperative control of the position and the force of the electro-hydraulic servo system is finally accomplished.

The invention discloses a novel least square support vector machine modeling method for a thermal error of a numerical control machine. The method comprises the following steps of: (1) selecting a kernel function and determining parameters; and (2) according to a principle of the least square support vector machine, establishing a machine thermal error model. A compensating system in the invention is simple in structure and reliable in application; and by means of the least square support vector machine modeling method, the model precision and the generalization capability are improved, and the defects of low precision, low generalization capability and the like of the conventional predicting method are overcome.

The invention provides a neural-network-based liver cancer auxiliary diagnosis system, method and device, and a medium. The neural-network-based liver cancer auxiliary diagnosis system, method and device convert explicit rules corresponding to the medical knowledge into implicit rules including connection weights by storing medical knowledge and a learning algorithm based on neural networks, and provide corresponding treatment plans for different diseases according to the implicit rules. The neural-network-based liver cancer auxiliary diagnosis system, method and device can improve the diagnosis of liver cancer simply and efficiently, can ensure the reliability of the diagnosis, and can effectively solve the limitations of doctors in the diagnosis and treatment of liver cancer due to doctors' inadequate and untimely grasp of standard diagnosis and treatment of liver cancer and interdiscipline knowledge, thus greatly improving self-learning ability of the system, and realizing constantself-improvement in the process of running.

The invention discloses a synthetic aperture radar (SAR) image change region detection method based on neighborhood ratio and self-stepping learning, and mainly solves the problem that the prior art is sensitive to speckle noise of an SAR image, and the problem that part of texture information of the SAR image is lost easily. The method comprises the specific steps of (1) reading an SAR image; (2)normalizing; (3) calculating a neighborhood ratio difference value; (4) constructing a difference value matrix; (5) selecting a training sample set; (6) training a deep belief network; (7) constructing a probability matrix; (8) updating a probability matrix; (9) obtaining a change detection image. According to the method, the local information of an original image and the self-learning capabilityof a deep belief network are effectively utilized, and speckle noise is reduced, the partial image information is kept, and the precision of change detection is improved.

The invention discloses a telephone number identification method and device, computer equipment and a computer storage medium. The telephone number identification method comprises the following steps:acquiring telephone identifiers of all first-class calling numbers; extracting first-type calling numbers of which all telephone identifiers are junk telephones and forming a number set; extracting frequency spectrums from the fourth digit to the seventh digit of all telephone numbers in the number set; and judging the second type of calling numbers as junk calls according to the frequency spectrum. According to the embodiment of the invention, the number characteristics of the junk phone can be effectively and efficiently identified by constructing the frequency spectrum of the number of thejunk phone, so that the junk phone judgment speed and accuracy are improved.

The invention discloses a remote sensing image target detection model establishment method based on context enhancement and an application, and belongs to the technical field of image processing, andthe method comprises the steps: building a to-be-trained target detection model based on a neural network, and carrying out the target detection and training on a remote sensing image, obtaining a remote sensing image target detection model based on context enhancement; in the target detection model, using each module for extracting a multi-scale feature map F of the remote sensing image; extracting global context information of the F to obtain M; respectively enhancing boundary information and category information in the F to obtain M<E> and M<E><cl> and respectively capturing informationassociation between channels in M<E> and M<E><cl> to obtain channel weights W<d> and W<c>; and fusing the M and M<E> according to the W<d> to obtain a boundary information enhanced feature map F<E>, fusing the M and M<E><cl> according to the W<c> to obtain a category information enhanced feature map F<E><cl>, and fusingF, F<E>, and F<E><cl> to obtain a feature map F<E><ct>, and carrying outtarget detection on the feature map F<E><ct>. The method can improve the target detection precision of a remote sensing image.

The invention relates to a transformer substation fault diagnosis method and diagnosis device based on improved case-based reasoning, and the method gives consideration to the correlation between thecondition attributes of all rules in a case library and the correlation between the condition attributes of other rules, and combines with a similarity measurement method of a weighted Euclidean distance. The method can continuously update the case library, improves the adaptability and accuracy of reasoning through the correlation between signals, has the self-learning capability of the system, and effectively improves the work efficiency of operation and maintenance personnel.

The invention relates to a board thickness intelligent control method based on active learning, which belongs to the field of intelligent control technology. Self-learnable performance of a nerve network is used as a theoretical basis. A dynamic nerve network is combined with active learning; the parameter of a PID controller is adjusted in an online manner; and a development model based on active learning is constructed, thereby establishing an intelligent control system for thickness of band steel, so that the board thickness control system can perform self adjustment at proper time, and the control performance of the board thickness control system is improved through continuous training of the dynamic nerve network. The board thickness intelligent control method has functions of providing a mathematical model with high generalization capability and wide application range for online control parameter adjustment of the system; combining active learning with the dynamic nerve network, and improving self-learning capability of the network through active learning and acquiring network training samples, thereby improving adaptive capability of the system and realizing intelligent in a real meaning.

The embodiment of the invention provides a lamp and a lighting control method thereof, and the lighting control method comprises the steps: carrying out the calculation of the lighting duration of thelamp N days before a statistical day, wherein N is an integer greater than 1 and less than 11; matching a to-be-executed dimming program from a plurality of preset dimming programs of the lamp according to the calculation result; and controlling an adaptive power supply of the lamp to be switched to the to-be-executed dimming program. According to the embodiment of the invention, the lighting duration of N days before a statistical day is calculated, and the to-be-executed dimming program is matched from a plurality of preset dimming programs of the lamp according to the corresponding calculation result; the adaptive power supply of the lamp is controlled to execute the matched to-be-executed dimming program to perform multi-mode lighting control on the lamp, so the diversity of lightingcontrol of the lamp is improved, the self-learning capability of the lamp is improved, and the maintenance cost of the lighting control of the lamp is reduced.

The invention discloses a home cleaning robot control system based on adaptive strategy optimization. The home cleaning robot control system comprises a sensing system, a control system, a cleaning system, a driving system and an electric power system, wherein the sensing system collects environment information and electric power system information and transmits the environment information and electric power system information to the control system; the control system processes the collected information through an adaptive strategy control method according to the collected information, and transfers control signals to the driving system and the cleaning system. The home cleaning robot control system processes environment information obtained from a self-sensing system by adopting an adaptive strategy optimization method in the control system of a cleaning robot, then selects appropriate action, and transfers control signals of the control system to the driving system and the cleaning system through the sensing system, so as to ensure that the cleaning robot executes corresponding action to clean home environment.

The invention relates to an underwater vehicle docking control method based on reinforcement learning, belongs to the technical field of ocean control experiments, and introduces a reliable boundary updated by new and old strategies based on a PPO algorithm framework in deep reinforcement learning to improve the stability of agent learning. Meanwhile, a self-adaptive rollback cutting mechanism is adopted, the rollback strength is adjusted in a self-adaptive mode according to the situation that task successful completion experience is collected, and therefore the upper limit and the lower limit of new and old strategy updating are adjusted, and an intelligent agent is encouraged to explore in the initial training stage and converge stably in the later training stage. In the aspect of simulation training, a docking training environment considering ocean current and ocean wave interference is constructed, the training environment is used for intelligent body learning, and the anti-interference capability of the underwater vehicle is greatly improved.

The invention provides a high-power microwave weapon cooperative air defense system contribution rate evaluation method. The method comprises the following steps: constructing a fuzzy wavelet neural network; selecting a fuzzy wavelet neural network input index and preprocessing the collected index data; training a fuzzy wavelet neural network by using the preprocessed data; utilizing the trained fuzzy wavelet neural network to evaluate an incoming target, and respectively obtaining first target combat effectiveness and second target combat effectiveness; and determining the system contribution rate of the high-power microwave weapon in the cooperative air defense combat by adopting a system contribution rate solving method based on combat effectiveness increment according to the first target combat effectiveness and the second target combat effectiveness. According to the method provided by the invention, the comprehensive gain of the high-power microwave weapon for cooperative air defense combat is intuitively obtained.

The invention discloses a multi-modal medical image fusion method, which comprises the following steps of 1) source image decomposition: performing two-stage decomposition discrete stationary wavelet transform on at least two source images, 2) source image fusion: fusing the at least two source images through seven enhanced radial basis function neural networks, and 3) inverse wavelet transform: performing inverse wavelet transform, through the inverse wavelet transform, converting the seven fusion sub-bands into a fusion image. The method has the advantages that through translation of the invariant multi-scale transformation operator, enough direction information is provided, and the algorithm is relatively simple and easy to operate. In order to enhance the self-learning ability of the neural network, in combination with medical image fusion, pixel values, regional energy, pixel gradients and regional average gradients are adopted to form an input layer of the neural network, information of target feature points is accurately extracted, and meanwhile, information loss is avoided.

The invention discloses a simulation practice teaching system for Chinese medical meridian circulation, and is applicable to the field of Chinese medical teaching, in particular to a Chinese medical meridian circulation drawing practice method based on a modern information technology. The system solves the problems that existing Chinese medical meridian teaching processes are monotonous and abstract, students have troubles in understanding the abstract meridian concept, students' interest in learning Chinese medicine is difficult to arouse, and bidirectional teaching interaction between students and teachers cannot be achieved. The method mainly comprises the following steps of a, selecting a meridian name for a coming circulation practice, and entering a meridian circulation practice mode; b, utilizing a touch control interaction module to conduct drawing with fingers; c, by means of a storage module, conducting path comparison; d, comparing a user drawing path with a correct meridiancirculation path by means of a logic verification module; e, comparing a user path with the correct meridian path through a display module to give a judgement result of this practice. According to the simulation practice teaching system for the Chinese medical meridian circulation, abstract Chinese medical meridian theoretical acknowledge is converted into a direct and interactive simulation training system.

A nuclear reactor early warning system includes a detection unit which measures various parameters of a nuclear reactor through sensors and detectors, converts the parameters into electrical signals and outputs the electrical signals, an input unit which is connected to a detection device and stores the electrical signals of the detection device into a preprocessing unit, the preprocessing unit which filters data signals, discards some wrong data and then stores the rest data into a critical heat flux database, a calculation unit which calculates corresponding critical heat flux values in realtime according to the measurement data filtered by the preprocessing unit, a self-learning unit which calculates the data in the critical heat flux database and continuously corrects a calculation model according to a principle of minimum error, a feedback unit which returns a result of a self-learning process to the calculation unit, and an output unit which displays a critical heat flux resultobtained by the calculation unit.