44results about How to "Strong approach ability" patented technology

The invention provides a wind energy converting system sliding mode control method and device based on a T-S fuzzy model. To solve the problem of the fault of an actuator in a wind energy converting system, the T-S fuzzy model is utilized for describing a nonlinear wind energy converting system with uncertain actuator fault information, the approximation accuracy of a controlled object is improved, and a good model foundation is established for sliding mode control. Meanwhile, by means of a sliding mode controller designed based on the linear matrix inequality technology, the stability of the wind energy converting system is guaranteed, and the robustness and fault tolerance of the wind energy converting system are improved. The precise tracking of the rotating speed of a power generator and the electromagnetic torque can be achieved when the uncertain actuator fault exists in the wind energy converting system, and the maximum wind energy capturing of the wind speed below the rated value is achieved, and a valuable reference scheme is provided for efficient and stable running of the wind converting system.

The invention relates to a method for predicting the thermal error of a machine tool spindle based on a genetic algorithm wavelet neural network, which belongs to the field of numerical control machine tool processing technology. A temperature sensor is reasonably arranged on a numerical control machine tool, and the temperature of a key temperature measuring point of the machine tool and the temperature data of a proces environment are measured by the temperature sensor, and the thermal error data of a machine tool spindle is obtained by a displacement sensor; the thermal error prediction model of machine tool spindle based on wavelet neural network is established after data processing, combining the advantages of genetic algorithm and wavelet neural network, the thermal error predictionmodel has the advantages of simple calculation, high precision, strong anti-disturbance ability and robustness, and has strong approximation ability and fast network convergence speed. The thermal error of the spindle of the CNC machine tool is effectively reduced, and the machining accuracy of the machine tool is improved.

The invention discloses a method for measuring the residual capacity of a battery in an online manner on the basis of particle swarm optimization. The method includes fully charging the lead-acid storage battery, cooling the charged lead-acid storage battery until the temperature of the lead-acid storage battery reaches the room temperature, discharging the lead-acid storage battery by a low constant current, and sampling and recording output voltages of a sensor and the residual capacity of the battery at fixed intervals; using data recorded in experiments as input data of a support vector machine and training and creating an SVR (support vector regression) model; solving parameters in the model by the aid of a particle swarm optimization algorithm to acquire a mathematical relation among the residual capacity of the battery and the output voltages of the sensor; combining the obtained relation among the residual capacity of the battery and the output voltages of the sensor with a currently measured output voltage of the sensor to acquire the residual capacity of the storage battery in the online manner. The method has the advantages that experiential knowledge and priori knowledge of designers are omitted, the residual capacity of the storage battery can be accurately and quickly acquired from the output voltages of the sensor by the aid of the few experimental data, the efficiency and the precision are high, and the method is high in practicality.

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 present invention relates to a traffic flow prediction method based on a GMDH neural network. The method comprises GMDH neural network offline traffic flow training and GMDH neural network onlinetraffic flow real-time prediction. The method provided by the invention employs a GMDH neural network algorithm to perform prediction of traffic flow at a traffic intersection, a general method is long in time in processing process of huge data and low in accuracy and is difficult to achieve requirements of real-time prediction of traffic flow; and the GMDH neural network online has a good approximation capability to divide the prediction of the traffic flow into two parts consisting of offline learning and online prediction, wherein the offline learning link combines a lot of data to performtraining of the neural network and learn the rule of traffic flow change, and the online prediction part calls the neural network which has complete learning to rapidly and effectively perform real-time prediction of the pass states of vehicles.

The present invention provides an XY motion platform contour control method and device on the basis of a fuzzy cerebellum model joint controller. The device comprises a voltage regulation circuit, a rectification filtering unit, an IPM inversion unit, an event manager of a digital signal processor DSP, a Hall sensor, a grating scale, a current sampling circuit, a position sampling circuit and an IPM isolation driving protective circuit. The DSP includes a position signal setter, a linear motor signal collector, a PI controller, an FCMAC controller and a driver. The XY motion platform contour control method adopts FCMAC to design a speed controller to reduce the tracking error so as to indirectly improve the contour processing precision of directly driving the XY motion platform. The XY motion platform contour control method and device on the basis of a fuzzy cerebellum model joint controller are applicable to the contour processing task of any locus and are able to realize the high-precision contour control and have good robustness.

The invention discloses a combined forecasting method of urban water demand based on minimum sum of square error. At first, a water demand database of the water supply pipe network is established. Then the RBF neural network model, GRNN model and ARIMA model are trained and established. Finally, combined forecasting is carried out based on the minimum sum of square error. The invention combines the characteristics of RBF neural network, such as strong approximation ability and global optimization, with the characteristics of GRNN neural network, such as fast learning speed and easy convergence, and with the characteristics of ARIMA, such as flexibility and strong adaptability, and combines the rolling renewal strategy, so that the prediction method can dynamically adapt to the developmentand change of the environment.

The invention discloses a power system probabilistic-optimal power flow calculation method based on a stacked denoising autoencoder. The calculation method comprises the following main steps that: 1)establishing a SDAE (stacked denoising autoencoder) optimal power flow model; 2) obtaining the input sample X of a SDAE optimal power flow model input layer; 3) initializing the SDAE optimal power flow model; 4) training the SDAE optimal power flow model so as to obtain a trained SDAE optimal power flow model; 5) adopting a MCS (Modulating Control System) method to carry out sampling on the randomvariable of a power system to be subjected to probabilistic power flow calculation so as to obtain a calculation sample; 6) inputting training sample data obtained in S5 into the SDAE optimal power flow model which finishes being trained in S4) in one time so as to calculate an optimal power flow online probability; and 7) analyzing the optimal power flow online probability, i.e., drawing the probability density curve of the output variable of the SDAE optimal power flow model. The method can be widely applied to the probabilistic-optimal power flow solving of the power system, and is especially suitable for an online analysis situation that system uncertainty is enhanced due to high new energy permeability.

The invention relates to a mask optimization method of a photoetching machine. According to the method, mask information expressed based on a pixel is converted to a frequency domain through discrete cosine transform, a low-frequency part is cut to be taken as an optimization variable and coded into particles, the sum of squared difference of each point between an ideal graph and a photoresist image corresponding to the current mask is taken as an evaluation function, and a mask graph is optimized by a particle swarm optimization algorithm. With the optimization method, the imaging quality of a photoetching system can be effectively improved, the optimization method has the advantages of simplicity in principle and relatively fast convergence speed, and is easy to implement, and the manufacturability of the optimized mask is high.

The invention discloses a blind equalization method for a wavelet neural network based on space diversity. On the basis of analysis of a space diversity technology and the equalization performance of a wavelet neural network, the method reduces the influence of fading by utilizing the space diversity and overcomes intersymobl interferences by using the stronger approximation capacity of a blind equalizer of the wavelet neural network. The invention overcomes the intersymobl interferences caused by the multipath propagation and the fading characteristic of a channel at a receiving end, improves the communication quality and has high convergence speed and small mean square error. The effectiveness of the method is verified by an acoustic channel simulation result. The method can effectivelyrealize the separation of signals and noise and the real-time restoration of the signals.

The invention discloses an intelligent monitoring system for multiple oil and gas concentration sensors based on Internet of things. The system consists of a gas station tank area environmental parameter acquisition platform based on a ZigBee network, and a gas station tank area environmental oil and gas concentration sensor monitoring subsystem. Through the adoption of the intelligent monitoringsystem, the problems that the gas station tank area environmental oil and gas concentration is not detected accurately and that an early warning is not given according to the characteristics of non-linearity, large lag and complex change of the gas station tank area environmental oil and gas concentration change in an existing gas station tank area environmental monitoring system is effectively solved, and the gas station oil and gas concentration prediction accuracy and robustness are improved.

The invention relates to the technical field of computational intelligence, in particular to an interval type II intuitionistic fuzzy random vector function connection neural network design method. The interval type II intuitionistic fuzzy random vector function connection neural network design method comprises the following steps: constructing an interval type II intuitionistic fuzzy random vector function connection neural network; adopting a hybrid learning method combining quantum clustering algorithm, swarm intelligence algorithm and least square method to adjust the structure and parameters of the neural network. The neural network adopts the membership function and the non-membership function based on the beta function as the forepart of the fuzzy rule, and adopts the random vectorfunction to connect the neural network as the afterpart of the interval fuzzy rule. The IT2IF-RVFLNN designed by the invention can achieve global approximation.

The invention discloses a green pepper greenhouse environment intelligent monitoring system based on a subtractive clustering classifier. The intelligent monitoring system is characterized by comprising a green pepper greenhouse environment parameter detection platform based on a wireless sensor network and a green pepper greenhouse yield intelligent early warning system. In the prior art, only adevice is adopted for monitoring the green pepper greenhouse environment parameter, and green pepper greenhouse yield cannot be warned according to green pepper greenhouse environment temperature andsunlight. The green pepper greenhouse environment intelligent monitoring system solve the above problem.

According to the ICPAES multi-peak spectral line separation method based on the particle swarm optimization, a mathematical model of a single spectral line is established. And constructing a multivariate evaluation function adaptive particle swarm algorithm to solve an optimal feature vector which can be used as a minimum value to analyze a target spectral line expression and an interference spectral line expression in a multi-peak spectral line, thereby carrying out interference correction. The method has the advantages that the optimal solution of the evaluation function is solved through the particle swarm algorithm to achieve ICPAES multi-peak spectral line separation, the overlapping interference curve and the target curve obtained through solving are accurate in characteristic parameter, the calculation result is easy to operate, errors can be reduced, and interference of overlapping spectral lines is avoided. Compared with a standard particle swarm algorithm, the adaptive particle swarm algorithm provided by the invention effectively ensures that the early iteration global exploration optimal solution and the later iteration local convergence to the global optimal solution,the convergence speed is high, the approximation ability is strong, and the performance is better.

The invention discloses a cucumber greenhouse temperature intelligent detection device based on an LVQ neural network. The cucumber greenhouse temperature intelligent detection device is characterizedby comprising a cucumber greenhouse environment parameter acquisition platform based on a CAN bus and a cucumber greenhouse temperature intelligent monitoring system. The device effectively solves the problem that the existing cucumber greenhouse monitoring system does not intelligently monitor and predict the temperature of the cucumber greenhouse environment according to the characteristics ofnonlinearity and large lag of the temperature change of the cucumber greenhouse environment, large area and complex temperature change of the cucumber greenhouse and the like, so that the regulation and control of the temperature of the cucumber greenhouse environment are greatly influenced.

The invention discloses a solar heat collection system photo-thermal efficiency prediction method based on GA-GRNN. The method comprises the following steps that 1), parameters of the solar heat collection system are determined; 2), training test data of a network are collected and distributed; 3), a GRNN structure is constructed; 4), an optimal smoothing factor sigma is determined through GA; 5),the GA-GRNN is trained so as to obtain a trained GA-GRNN; 6), the GA-GRNN is tested, and the test data selected in the step 2 are input into the GA-GRNN which is trained in the step 5 for testing; and 7), the photo-thermal efficiency prediction is carried out by using the GA-GRNN trained in the step 6 to obtain the photo-thermal efficiency prediction result of the current solar heat collection system. According to the photo-thermal efficiency prediction method, the uncertainty of weather and climate factors can be remedied, the difficulty of data collection is greatly reduced, and the photo-thermal power of the solar heat collector can be accurately predicted.

The invention provides a porosity prediction method based on selective ensemble learning. According to the method, a typical machine learning method is researched and analyzed; a group of individual learning models with excellent performance are selected from classic models such as a support vector machine, a radial basis function (RBF) neural network, a random forest, ridge regression and K-nearest neighbor regression through a principal component method analysis method to form an integrated learning model, wherein the weight of the individual in the integrated model is obtained by a principal component weight averaging method, and finally, the output of the integrated learning model is obtained by adopting a weighted average method. The model is called a PCA-SEN model for short. The PCA-SEN model overcomes the defects of a single model, and the generalization ability of the model is high. The reservoir porosity is predicted through the method, so that a more accurate prediction result is expected to be obtained.