6 results about "Learning factor" patented technology

The present application relates to the technical field of pipeline corrosion, and particularly relates to a method and device for predicting pipeline corrosion rate based on an IPSO-BP neural network model, an electronic device and a storage medium. The IPSO-BP neural network model is a hybrid algorithm. The IPSO algorithm adjusts the inertia weight factor, individual experience learning factor and social experience learning factor in the PSO algorithm in a nonlinear decreasing manner, so that the PSO algorithm can balance the relationship between global search and local search in the search process, thereby improving the convergence and search ability of the PSO algorithm, and better optimizing the BP neural network model.

The application relates to a control method for dynamic walking of a biped robot and the biped robot, and belongs to the technical field of robot control, which comprises the following steps: 1, a self-recurrent cerebellar model neural network is used to establish a dynamic model of the biped robot with a disturbance term, and dynamic robust walking of the biped robot is converted into a problem of realizing stability of a multi-input multi-output nonlinear system with a bounded uncertain term; 2, an adaptive self-recurrent cerebellar model neural network error observer is designed to estimate an error upper limit; 3, an adaptive law of network weight is designed to realize real-time updating of the network weight space and to adjust parameters of each learning factor; and 4, a boundary value estimation algorithm is used to compensate for an estimation error and feedback to a robot walking system, so that the biped robot can realize asymptotic stable walking. The application enables the control system to adapt to time-varying characteristics of the biped walking system on line, and has continuous learning and adaptive capacity for unknown dynamics.

The present application relates to a kind of particle swarm-based clean energy station multi-unmanned aerial vehicle task allocation method, comprising: obtaining clean energy station area data information, and the division of patrolling area is carried out;Clean energy station multi-unmanned aerial vehicle task allocation model is constructed;According to individual optimal position and global optimal position, the speed and position of particle are adjusted, and objective function is optimized;When the iteration number reaches upper limit, the task allocation solution corresponding to global optimal particle is returned, otherwise, continue to update particle state, optimization solution.Nonlinear dynamic collaborative improvement is carried out to inertia weight and learning factor, inertia weight adopts nonlinear self-adaptive decreasing strategy, iteration initial period is kept larger value, enhances the global traversal ability of unmanned aerial vehicle formation, widely searches various sub-regions and task combination;Rapidly attenuate in iteration later period, strengthen local precision search, ensure fast convergence to optimal cost combination.Golden sinusoidal algorithm is fused to reconstruct position update mechanism, realize the dynamic balance of global and local search.

This invention belongs to the field of electronic interference technology, specifically relating to a method, program, device, and storage medium for allocating interference equipment. The invention designs a binary magnificent wren-warbler algorithm, which initializes the population's positional distribution using chaotic mapping. It judges the algorithm's local convergence trend by using the average Euclidean distance and fitness change rate. By introducing inertia weights and learning factors, the local search function is improved, enhancing the algorithm's local search capability. Combined with the algorithm's global search capability, a new fitness function is constructed to improve algorithm performance. Furthermore, activation functions and thresholds are used to convert continuous values ​​into discrete values, enabling the algorithm to solve discrete problems and enhancing its generalization and the rationality of interference equipment allocation. This invention solves the problems of existing interference equipment allocation methods' inability to respond quickly and to allocate interference resources rationally. Upon receiving a radiation source signal, this invention can immediately generate interference equipment allocation results, achieving rapid interference response.

This invention belongs to the field of intelligent control and path planning for unmanned aerial vehicles (UAVs), specifically involving a multi-strategy particle swarm optimization (PSO) method for UAV trajectory planning based on reinforcement learning. The method includes: first, constructing an environmental threat model based on 3D elevation data and establishing a multi-objective evaluation function encompassing path length, flight altitude, path smoothness, and collision threat; then, introducing a Q-learning reinforcement learning mechanism into the PSO algorithm to construct a state-action mapping and adaptively adjust the learning factor, while designing nonlinear dynamic inertial weights to balance global search and local exploitation; finally, using a reinforcement learning-driven multi-strategy PSO optimizer to iteratively optimize the 3D trajectory and output the optimal flight path. This invention provides an optimization scheme that balances flight safety and path efficiency for trajectory planning problems in complex mountainous environments and threat areas, possessing good practical value and system reliability.

The application relates to the technical field of unmanned aerial vehicle navigation, and discloses a long aircraft screening method for an unmanned aerial vehicle cluster hierarchical cooperative navigation system, which comprises the following steps: obtaining state data of each wing aircraft in the system according to a self sensor to determine the azimuth angle and the pitch angle of each wing aircraft relative to each long aircraft, and constructing an observability matrix; performing singular value decomposition on the observability matrix, and determining an observability index according to the maximum singular value and the minimum singular value after the decomposition, which is used for quantifying the observability degree of the long aircraft type of a long aircraft combination used in the unmanned aerial vehicle cluster hierarchical cooperative navigation system to the wing aircraft navigation error contribution; and based on a binary particle swarm optimization algorithm, taking the observability index of the unmanned aerial vehicle cluster as an adaptive function, iteratively searching for a target long aircraft combination, wherein in the iteration, the inertia weight of the algorithm is continuously adjusted according to the relationship between the particle fitness value and the group fitness value, and the individual learning factor and the group learning factor are continuously adjusted according to the iteration number.