142results about How to "Strong non-linear mapping ability" patented technology

The invention discloses a power grid security situation predicting method based on an improved deep learning model and belongs to the technical field of power system safety. The power grid security situation predicting method includes: performing power grid security situation evaluation through power grid data collection and preprocessing; aiming at the characteristic that indicator data of power grid security situation evaluation are high in relevance and dimension, providing an improved self-coding network method to lower dimension of the indicator data, and utilizing a data sample after dimension reduction and a power grid security situation value corresponding to a next time monitoring point; adopting an improved deep belief network to build a deep learning situation predicting model with multi-input and multi-output for power grid security situation prediction. By the power grid security situation predicting method, speed and accuracy of power grid security situation prediction can be improved effectively.

The invention discloses a nonlinear self-adaption control method of near-space hypersonic vehicle (NHV), which belongs to a flight control method in the technical field of aerospace. The control method mainly comprises three control law parts: a nominal nonlinear generalized predictive control law (NGPC), a B-spline recursive functional linkage network (BRFLN) self-adaption control law, and a robust control law of gain self-adaption adjustment. The invention integrates the simplicity of the NGPC method and the effectiveness of dynamic uncertainty of the BRFLN learning, targets the immeasurable dynamic uncertainty and fast interference of an attitude system in the flight of the NHV, brings favorable learning effect, and realizes the nonlinear accurate control to the attitude angle.

The invention provides a speech error detection method by front-end processing using an artificial neural network (ANN). The method comprises the following steps: extracting new 64-dimensional features with strong pattern recognition capability and good discrimination property from 39-dimensional mel-frequency cepstrum coefficient (MFCC) parameters by utilizing a multilayer perceptron (MLP); performing speech recognition on test data by a machine, and generating a goodness of pronunciation (GOP) score; and pointing out pronunciation errors and error degree according to a set threshold, and performing directed-learning for pronunciation errors.

The invention discloses a Hadoop framework-based short-term load prediction method for a distributed BP (Back Propagation) neural network. The method specifically comprises the steps of obtaining an initial load data set; dividing the load data set into small data sets and storing the small data sets in data nodes of a distributed file system; initializing BP neural network parameters and uploading a parameter set into the distributed file system; training the BP neural network according to a current load sample, and obtaining correction values of a weight and a threshold of the BP neural network in the current data set; performing statistics on sum of weight and threshold parameters of all layers and between the layers of the network according to a key value of a key value pair; judging whether the convergence precision or the maximum iterative frequency is reached or not in a current iterative task, and if yes, establishing a distributed BP neural network model, or otherwise, performing correction of the weight and threshold parameters of the network; and inputting prediction day data and obtaining load power data of a prediction day. According to the method, the load prediction speed is increased and the requirements of load prediction precision are met.

The present invention discloses a climate change prediction method and system for fitting various climate modes based on a modified BP neural network. The method comprises the following steps: collecting rainfall data that characterizes a climate change feature; performing downscaling processing on climate mode prediction grid data, so as to obtain a long series of data with the same resolution as measured data; performing average correction on a downscaled climate mode prediction series; establishing a modified BP neural network model and determining a network model structure; training and inspecting the established BP neural network model; and predicting a future climate spatial-temporal change using a model output. According to the method and system disclosed by the present invention, output/input data processing of the BP neural network is modified, and various climate modes are fit to predict the future climate change, so that reliability of the prediction result is increased, and the defects that the deviation of a single mode is relatively large during an assessment and that basin space nonuniformity is not considered during a multi-mode collection assessment and the like are made up.

The invention discloses an oilfield pumping unit oil pumping energy saving and production increasing optimization method based on the back propagation neural network (BPNN) and the strength Pareto evolutionary algorithm 2 (SPEA2). The method is characterized by including the following steps: step 1, calculating decision variables X; step 2, collecting samples of power consumption and samples of oil production Y to acquire a sample matrix; step 3, building a process model of oil pumping of a pumping unit; step 4, optimizing each decision variable in the range of an upper limit and a lower limit of each decision variable by using the SPEA2 based on a BPNN model; step 5, guiding actual production if the power consumption is reduced and the oil production is improved, and if not, returning the process to the step 1, changing S1 decision variables X on purpose and screening the decision variables X again; and step 6, assigning S1+1 to the S1, and returning the process to the step 1 if the combination of the set S1 decision variables X can not enable the power consumption to be reduced and the oil production to be improved. The oilfield pumping unit oil pumping energy saving and production increasing optimization method based on the BPNN and the SPEA2 has the advantages that an optimal value of technological parameters can be determined, and actual production guiding can be carried out according to the optimized technological parameter optimal value.

The invention discloses a method for estimating the state of health (SOH) of a lithium battery based on a grey neural network. The method of the invention includes the following steps of: respectivelyperforming constant-current discharge and pulse discharge on the battery, and recording battery capacity data in the constant-current discharge process and the battery terminal voltage and dischargecurrent in the pulse discharge process of the battery; analyzing the characteristics of the battery terminal voltage, and building a third-order RC model in Simscape as an equivalent circuit model ofthe battery; automatically estimating internal impedance parameters of the battery through the battery model; constructing a battery SOH estimation model combining the grey theory with a neural network, and training the model according to the recorded internal impedance parameters of the battery and the battery capacity; and estimating the battery capacity by the model, and further calculating theSOH of the battery. The method of the invention can adapt to the highly nonlinear characteristics of an electrochemical system of the battery, and has the advantages of small data calculation amount,less required sample data, high prediction accuracy and the like.

The invention, which belongs to the technical field of multi-sensor information fusion, discloses an evidence-synthesis-based information-fusion target recognition method. A plurality of sensors are used for carrying out attribute information collection on a to-be-identified target and a feature attribute is extracted from the collected attribute information; data having the feature attribute aredivided into training data and testing data, wherein the training data are used for constructing a neural network model and the testing data are used for obtaining a basic probability assignment value; and then evidences are synthesized based on an improved evidence synthesis method and the synthesized result is used as the target recognition basis. According to the invention, the basic probability assignment values of evidences are obtained accurately and a synthesis problem of high-conflict evidences is solved. The basic probability assignment values of evidences are obtained by using the neural network and the neural network has the high nonlinear mapping capability and is capable of mapping the intrinsic relationship between the target feature data, so that the accuracy of the basic probability assignment values is ensured, the conformance to the real scene is realized, and the practical significance is good.

The invention discloses a machine tool self-adaptive control method based on a GA-BP neural network algorithm, and belongs to the technical field of numerical control machining. The method comprises the steps that a spindle motor power signal and a spindle vibration signal in the workpiece numerical control machining process are monitored in real time, and the feeding speed and the spindle rotating speed are optimized in real time and adjusted adaptively based on the spindle power signal and the spindle vibration signal; and the whole signal acquisition process does not influence normal processing. The machine tool self-adaptive control method based on the neural network algorithm can be used to effectively improve the machining efficiency and the machining quality, prolong the service life of a cutter and a machine tool and reduce the cost.

The invention discloses a GNSS receiver combined interference classification and identification method based on a two-stage neural network. A receiver receives navigation signals sent by N visible satellites; according to a received GNSS signal model and interference source, a two-stage identification scheme based on a BP neural network is adopted to extract time domain and frequency domain characteristics of a digital intermediate frequency signal after A/D conversion through a first-stage identification module, and the time domain and frequency domain characteristics are sent to the BP neural network for suppressing interference detection and classification; if an identification result of the first-stage recognition module is that there is no interference or deception jamming, the digital intermediate frequency signal is captured, related peak characteristics are extracted by using a captured two-dimensional search matrix, and the related peak characteristics are sent to the second-stage recognition module for deception jamming detection; and when a final identification result of the two stages of identification modules is that there is no interference, it is determined that thereceived signal is a real satellite signal, and after the interference type is identified, a corresponding interference processing means is adopted. The method can be used to quickly and accurately identify interference.

The invention discloses a method and a device for determining gas pressure of a coal seam. The method comprises the following steps: at an initial stage after hole drilling and hole sealing, a drilled hole gas emission rate q meets a power function relation along with time t, namely q=At-B; in the formula, a drilled hole gas emission initial speed A and an attenuation coefficient B are parameters which are changed along with the change of the gas pressure of the coal seam; and a relation is utilized to carry out rapid inversion on the gas pressure of the coal seam through a BP neural network. According to the device for determining the gas pressure disclosed by the invention, an air inlet pipe, a yellow mud hole sealing opening and an air guide pipe are arranged in a measuring room; a flow sensor is connected with the air guide pipe, a vacuum pump and a gas concentration sensor; an air outlet pipe is mounted on the vacuum pump; a signal conditioning circuit is connected with the flow sensor and the gas concentration sensor; an A/D (Analogue/Digital) converter is connected with the signal conditioning circuit; and a PC (Personal Computer) is connected with the A/D converter. The method and the device disclosed by the invention have the characteristics of solving the difficulty that the height of a function relationship for indirectly determining the gas pressure of the coal seam, and having fast inversion.

The invention provides a method for soft measurement of the effluent total phosphorus (TP) in the sewage disposal process based on the neural network, and belongs to the field of sewage disposal field. The mechanism is complex in the sewage disposal process, and to enable a sewage disposal system to be in a good running working condition and to obtain the higher effluent quality, the procedure parameters and the water quality parameters in the sewage disposal system need to be detected. The invention provides a soft measurement model established based on the self-organization radial-based neural network to solve the problem that the effluent total phosphorus of a current sewage disposal plant cannot be obtained in real time. The initial structure and the initial parameters of the neural network are determined according to the self-organization method, the structure of the neural network is simplified, and real-time soft measurement is carried out on the effluent TP. According to the soft measurement result, the related control link in the sewage disposal process and materials in the biochemical reaction are adjusted, the quality of the effluent obtained after sewage disposal is improved, and a theoretical support and a technological guarantee are provided for safe and stable running in the sewage disposal process.

The invention discloses an intelligent granary environment safety monitoring system based on a field bus. The system is composed of a granary environment parameter collection platform based on a CAN field bus, and a granary environment safety evaluation subsystem. The system realizes the intelligent detection of granary environment parameters and the intelligent evaluation of granary environment safety. Many problems still existing in the granary environment caused by the unreasonably designed and poor traditional granary environment multi-parameter detection equipment, the incomplete detection system and the like are solved. Based on the nonlinearity and large lag of granary environment parameter changes and the large area and complex structure of the granary environment, the defects of inaccuracy, low reliability and the like of a granary environment monitoring system are overcome, accurate detection and reliable classification of granary environment parameters are realized, and therefore, the accuracy and robustness of granary environment parameter detection are greatly improved.

The invention discloses a short-term load predicting method for an Elman neural network based on an improved ABC algorithm. The short-term load predicting method comprises the following steps: takinga series of improving measures specific to defects such as low converging speed of an artificial bee colony (ABC) algorithm and poor developing performance of a searching equation after forward transmission of an input signal of the conventional Elman neural network, backward transmission of an error signal and a delay operator of a carrying layer are fully analyzed, wherein the improving measuresinclude re-designing a searching equation, adjusting the honey searching frequency and changing the selection mechanism of an optimal solution and the like; applying an optimal weight generated by the improved ABC algorithm and a threshold value to the Elman neural network to realize short-term load prediction on a power system, and increasing the load prediction speed; and lastly, implementing aload prediction function in MATLAB, and optimizing the weight and the threshold value by adopting the improved ABC algorithm according to an experiment result, so that the maximum prediction absoluteerror is lowered remarkably.

The invention designs a diagonal recurrent neural network (DRNN) control strategy (Q-DRNN) based on a Q learning algorithm. The Q-DRNN organically combines the strong search capability of Q learning with the advantages of the built-in recursive ring structure, the dynamic mapping capability, the adaptive time-varying characteristic and the like of the DRNN, and is used for improving the working stability of a brushless direct current motor (BLDCM). In the Q-DRNN, the DRNN iterates an output variable through a unique recursive ring in a hidden layer, and optimizes the key weight of the output variable so as to accelerate the iteration speed; and meanwhile, the improved Q learning is introduced to correct the weight term factor of the DRNN, so that the DRNN has self-learning and online correction capabilities, the anti-interference capability and robustness of the system are enhanced, and the brushless direct current motor achieves a better control effect.

The invention discloses a fan gearbox performance detection and health evaluation method based on temperature parameters. According to the method, fan state detection data are analyzed, the operationcharacteristics of a wind turbine generator unit are combined, state detection and fault prediction are carried out on the temperature of a gearbox and the temperature of a gearbox bearing by adoptinga model established by combining KECA and GRNN, an evaluation decision is performed on the running state of the fan gearbox through ideas of threshold discrimination and statistical process control according to predicted residual error, and a gearbox health state result is obtained by adopting comprehensive fuzzy discrimination. When the method is applied to an actual wind turbine generator unitcase with small sample data, the health state of the wind turbine generator unit can be accurately predicted and evaluated. The method is applied to an actual wind turbine generator unit with small sample data, and the health state of the wind turbine generator unit can be accurately predicted and evaluated.