119 results about "Learning factor" patented technology

The invention discloses a global self-adaptive gray level image enhancement method based on double gamma correction, which adopts a particle swarm optimization algorithm combined with an image doublegamma function to carry out global double gamma correction, and mainly comprises the following steps of: inputting a gray level image and initializing parameters of the particle swarm optimization algorithm; performing double-gamma correction on the input image by adopting each particle position to obtain a preliminary enhanced image, calculating a corresponding fitness value, and updating a historical optimal fitness value and an optimal position of the particle individual and the group; judging whether an iterative optimization termination condition is met or not, and if not, updating the inertia weight, the learning factor, the speed and the position of each particle and continuing iteration; otherwise, performing double-gamma correction on the input image by using the final group optimal position to obtain a final enhanced image. When the low-illumination grayscale image is enhanced, the contrast of the image can be effectively improved, excessive enhancement of a local bright areais avoided, the texture and detail information of the enhanced grayscale image are clear and complete, and the overall visual effect is improved.

The invention discloses a multilayer-perceptron training method based on a bee colony algorithm with a learning factor. In the method, an idea of asynchronous adjustment of the learning factor is introduced to update a nectar source position of following bees so that the self-adaptive optimization capability of a bee colony is strengthened and single extreme values and a global extreme value of the colony are used to improve a search mode in the manual bee colony algorithm so that search efficiency and performance are optimized so as to achieve an optimal optimization effect of an initial weight and threshold of a multilayer perceptron and improve the predication precision of the multilayer perceptron.

An air conditioner includes: a blower configured to discharge air, the blower being connected to an outdoor unit, a parameter generator configured to generate at least one parameter during a time period for which the air conditioner is operated with a first cooling capacity based on a set temperature, a learning unit configured to receive the generated at least one parameter as a learning factor and generate operation mode information, an operation mode controller configured to control at least one of the blower or the outdoor unit based on the generated operation mode information, and a central controller configured to control the parameter generator, the learning unit, and the operation mode controller. The air conditioner is operated with a second cooling capacity after the air conditioner is operated with the first cooling capacity for the time period, the second cooling capacity being different from the first cooling capacity.

The invention discloses a robot path planning method based on spider colony algorithm, comprising of: modeling the map environment by the grid method; initializing the basic parameters of the spider colony algorithm; randomly assigning the initial spider colony; the spider colony completing the collaborative process according to the learning strategy of the spider colony algorithm; outputting fitness value and determining whether the fitness value is less than the set minimum fitness value; and outputting a shortest path and a length. The invention optimizes the neighborhood search and the global search by introducing the inertia weight and the learning factor, and increases the interaction between the inertia weight and the learning factor to balance the global search and local development capabilities of the method. The improved method has more stable convergence results, improves convergence speed and search accuracy, and palns the best shortest path.

The invention provides an economic optimization method of a microgrid containing wind power and photovoltaic power generation. The method comprises the following steps: constructing a microgrid operation data set; establishing an operation optimization target function; establishing a constrain condition of the operation optimization target function; constructing an improved particle swarm optimization algorithm model, and outputting an optimal location; and obtaining the least cost of the optimal solution according to the optimal location and the operation optimization target function, and accomplishing the microgrid economic optimization. Through the microgrid economic optimization method provided by the invention, the diversity of the particle is increased, the capacity of searching theglobal optimum is increased, and the method is hard to trap into the local optimum. And meanwhile, the searching capacity on the optimal solution by each particle is further improved by adopting self-adaptive inertia weight and learning factors, better optimization effect can be acquired when performing economic optimization on this cooling-heating-power cogeneration microgrid, the problem that the economic optimization problem is easy to trap into the local optimum is effectively solved, and the better economic optimization effect is acquired.

The invention discloses a sensor node coverage method based on an improved swarm algorithm. Swarm individuals of different initial points are constructed according to different layout of nodes in a sensor area, the area coverage of the swarm individuals is used as an optimized target function, the asynchronous change idea of learning factors is integrated to the traditional swarm algorithm in the search process, and a search population is guided to jump out of local optimum by sufficiently using historical information and global information of a swarm in the search process, so that the optimization search efficiency and performance are greatly improved, premature convergence of the algorithm is avoided, and intelligent layout optimization of sensor network nodes is realized.

The invention provides an optimized scheduling method and device for a micro-energy grid of an intelligent agricultural greenhouse. The method comprises steps as follows: a scheduling optimizing model of the micro-energy grid is established on the basis of energy flowing models of micro-energy in the micro-energy grid of the intelligent photovoltaic facility agricultural greenhouse with the purpose that the comprehensive operation cost of the micro-energy grid in a day is lowest; the scheduling optimizing model is solved with a dynamic learning factor type second-order oscillation cultural particle swarm algorithm, and the operation scheduling strategy of the micro-energy grid is obtained. According to the optimized scheduling method and device for the micro-energy grid of the intelligent agricultural greenhouse, the scheduling optimizing model is solved with the dynamic learning factor type second-order oscillation cultural particle swarm algorithm, so that the scheduling strategy is obtained, the method and the device have higher operating speed, higher global searching ability and better convergence, and economical operation of the micro-energy grid of the intelligent photovoltaic facility agricultural greenhouse can be realized.

The invention discloses a virtualized wireless network function editing method based on economic benefits. According to the method, a virtualized wireless network function arrangement mathematical optimization model is established, and a virtualized wireless network function arrangement process (VNFPSO) based on economic benefits is provided. Aiming at the openness of a virtualized wireless accessnetwork architecture, the discreteness of a network function and the exponential appearance of a network load, the classical particle swarm optimization algorithm is corrected in the aspects of inertia weight, particle variation, learning factors and the like, and the solving speed of a global approximate optimal solution is increased. According to the invention, the service rejection rate of thevirtualized wireless access network is reduced, and the utilization rate of network system resources is improved.

Provided is a multi-target integrated scheduling method for multi-place transportation in a cloud manufacturing environment. Most of the current cloud manufacturing resource scheduling methods often consider only one optimization objective and ignore the impact of transportation factors on scheduling plans. The method of the invention takes service time, service cost, quality of service and satisfaction evaluation as an objective function, and fully considers cross-region manufacturing resource transfer factors. The method mainly includes the steps as follows: carrying out initialization through a process and resource-based double-layer encoding mode; adding a dynamic learning factor to guide the population search range and accuracy; and updating a global optimal solution set through non-dominated sorting, and introducing a dynamic selection factor to improve the distribution uniformity of a non-dominated solution set. Through the method, a detailed processing plan for a processing task can be obtained. The method is used to solve the multi-target scheduling plan problem in a cloud manufacturing environment. The utilization efficiency of resources is improved, and the processing cost of users is reduced.

A remains 3D reconstruction method based on a frontal image is characterized by a single picture 3D face reconstruction technology based on a statistical model. The method comprises creating a 3D facestatistical model based on a 3D face database in advance, and training a 2D face feature point parameter model by using a SDM algorithm based on a 2D face database. During the face reconstruction based on a single frontal image, the face feature points are extracted first by using a two-dimensional image feature point parameter model; then, an energy function is subjected to iterative optimization by an learning factor adaptive gradient descent method based on the three-dimensional face statistics model to obtain the parameterized vector of the statistical model, that is, the three-dimensional face model corresponding to the two-dimensional face image is obtained. Results show that the face reconstructed by the method has high similarity.

The invention discloses a screw type material discharging device based on variable-rate learning and a controller of the screw type material discharging device. The discharging device comprises a rack, a discharging bin, a screw conveyor, a metering hopper, a weighing module, a discharging valve, a material mixing hopper, a controller, a material storage in and a feeding pump. The upper portion ofthe metering hopper is provided with a funnel-shaped material distributor, and the discharging bin is internally provided with a distance sensor and a stirrer. The controller is used for controllingthe screw conveyor through iterative learning based on the discharging error of the single time and the accumulated discharging error, and dynamic adjustment is conducted on the learning factor of thedischarging error of the single time and the learning factor of the accumulated discharging error based on the changes of the discharging error in the iterative process. By means of the screw type material discharging device based on variable-rate learning and the controller of the screw type material discharging device, the distance sensor and the stirrer are used for detecting and the adjustingmaterial accumulation in the discharging bin, stability of compactness of materials is ensured, and the learning factor can be automatically adjusted and optimized. Compared with the prior art, repeated parameter cut and try are not needed, an iterative approach good in convergence performance can be rapidly obtained, and falling materials can be effectively utilized in the iterative learning process.