36results about How to "Global optimization" patented technology

The invention discloses an intelligent cable automatic manufacturing machine based on a fuzzy support vector machine. The intelligent cable automatic manufacturing machine is characterized by comprising a wire inlet device (1), a peeling and cutting device (2), a stripping and twisting device (3), a wire clamping mechanism (4), a rotary conveying device (5), a needle pressing device (6), the wireinlet device (1), the peeling and cutting device (2), the stripping and twisting device (3), the wire clamping mechanism (4), the rotary conveying device (5), and the needle pressing device (6) construct an adjusting platform for adjustment and control through a single-chip microcomputer, a rotary drum rotating speed intelligent cascade control system based on the fuzzy support vector machine is designed in the adjusting platform to carry out the closed-loop control of the rotating speed on the pulling drum (14) of the wire inlet device (1). According to the invention, the problems that the automation degree of existing cable manufacturing equipment is not high, most procedures need manual participation, the working efficiency is low, and a certain safety accident can be caused are effectively solved, and the problem that the rotating speed precision of the pulling drum of the wire inlet device is poor is effectively solved.

The invention discloses a fractionally-spaced multi-modulus algorithm (FS-MMA) based on a DNA genetic bat algorithm (DNA-GBA) (DNA-GBA-FS-MMA). The DNA-GBA-FS-MMA combines the DNA-GBA with the FS-MMA and fully utilizes the advantages of the FS-MMA and the DNA-GBA. The invention introduces a DNA genetic algorithm into a bat algorithm (BA) so as to obtain the DNA-GBA. A global optimal position vector of a bat group is found by the DNA-GBA and is used as the real part and the imaginary part of the initialized optimal weight vector of a MMA in order to increase rate of convergence and reduce excess mean square error. Compared with a BA-FS-MMA and a BA-MMA, the DNA-GBA-FS-MMA is fast in rate of convergence, low in mean square error, and global optimal performance, and has practical value in communication technology field.

The invention provides an intelligent distributed line selection system and a line selection method. The system includes the following parts: model generation component, information collection component, attribute weight prediction component, attribute action component, screening component, intelligent line selection component, evaluation Output component: the line selection model is generated by the model generation component, and the importance of different attributes can be obtained by calculating the weights of different attributes of the initial control point, so as to obtain the influence of the control point in different aspects, which is convenient for a comprehensive understanding of the control point and Further analysis ensures the accuracy of line selection.

The invention discloses a combined prediction method of urban water demand based on the minimum error square sum. The invention first establishes the water demand database of the water supply pipe network. Then train and build RBF neural network model, GRNN model, ARIMA model. Finally, combined forecasting is performed based on the minimum sum of squared errors. The invention combines the characteristics of RBF neural network with strong approximation ability and global optimality with the characteristics of GRNN neural network such as fast learning speed and easy convergence with the characteristics of ARIMA flexible and strong adaptability, and combined with rolling update strategy, so that the prediction method can dynamically adapt The environment develops and changes.

A face gender recognition method based on Fireworks Deep Belief Network, which consists of original image preprocessing, training Deep Belief Network, optimizing the initial parameter space of Deep Belief Network with Fireworks Algorithm, and using the Deep Belief Network optimized by Fireworks Algorithm for face gender recognition . The present invention adopts the deep belief network to learn more abundant features of face image semantic information in the unsupervised stage, uses the firework algorithm to adjust the initial parameter space of the deep belief network in the supervised stage, and obtains a network model more suitable for recognition tasks. The invention has the advantages of strong anti-interference, global optimization, high recognition rate, etc., and can be used for face gender recognition and other image recognition and classification.

The invention relates to a method for improving the reliability of key users' power consumption based on active power source searching, and belongs to the technical field of power grid management. First, according to the real-time operating status of the system, obtain the real-time outage probability P of each component in the relevant generation / transmission / transformation / distribution system; according to P, calculate the load loss probability p of the node where the key user is located in the power generation and transmission system L ; according to P, P L , calculate the load loss probability p of all outgoing lines of the substation system supplying power to key users f ; According to P, p f , to calculate the power outage probability of key users Calculate all feasible variable, power distribution and power supply schemes Take the scheme corresponding to the minimum value as the high-reliability power supply scheme. This method takes into account the characteristics that the user's power supply reliability changes from time to time with the system's operating status. By adjusting the network topology of the substation and power distribution system, it dynamically provides power supply solutions for key users according to local conditions, so as to ensure the key users' safety in any operating state of the system. Electricity risk is always kept low.

The invention discloses a transient electromagnetic inversion method based on transverse smooth constraint and is applied to the earth exploration field. The method comprises steps that S1, a primary pulse magnetic field is emitted to the underground, a secondary magnetic field excited by induction eddy current generated by an underground conductive geologic body under the excitation action of the primary pulse magnetic field is measured, secondary magnetic field data acquired through measurement is taken as observation data; S2, an initial inversion model is constructed; S3, a mismatching functional between the observation data and forward theory data is constructed; S4, a vertical constraint functional for constraining electric parameter change of the conductive geologic body is constructed; S5, a transverse constraint functional for constraining electric parameter change of the conductive geologic body among measurement points is constructed; S6, an inversion target function is constructed on the basis of the mismatching functional, the vertical constraint functional and the transverse constraint functional; and S7, the initial inversion model is taken as initial conditions, an electric parameter corresponding to a smallest inversion target function value is solved, and the underground structure is reconstructed according to the electric parameter. The method is advantaged in that the underground structure acquired on the basis of inversion imaging results is more accurate and reliable.