11 results about "Attractor" patented technology

An optimization device according to one aspect of the present disclosure is for determining the best solution of an optimization problem on the basis of particle swarm optimization, and comprises: a setting unit that sets, for each particle, an attractor that represents a target position in a search space for the best solution, by using the position of each particle until the current iteration in the particle swarm optimization and an objective function value at the position; and a speed update unit that updates the speed of each particle in the particle swarm optimization by using the attractor set for each particle.

An optimization device according to one aspect of the present disclosure is for determining the best solution of an optimization problem on the basis of particle swarm optimization, and comprises: a setting unit that sets, for each particle, an attractor that represents a target position in a search space for the best solution, by using the position of each particle until the current iteration in the particle swarm optimization and an objective function value at the position; and a speed update unit that updates the speed of each particle in the particle swarm optimization by using the attractor set for each particle.

The application relates to encryption communication technology, and particularly discloses a construction, dynamic analysis and application method of a six-dimensional memristor hyperchaotic system. The method discovers that the new system has unique dynamic behaviors through a phase diagram, a bifurcation diagram and a Lyapunov exponent spectrum, generates symmetric coexistence cycles, chaos and hyperchaos double-wing attractors which depend on system parameters, shows extremely strong initial value sensitivity, has special symmetric extreme multistability and a memristor initial value enhancement behavior, generates symmetric four-wing attractors by adding a constant controller, shows more complexity and diversity, and finally, simulation is carried out through Multisim, the generated attractor is consistent with a phase diagram obtained through numerical simulation, and the feasibility of the new system is verified. The application has great advantages in encryption security and attack resistance.

The application discloses a multi-scene HNN construction method and an adaptive synchronization method, wherein the non-polynomial memristor constructed by the multi-scene HNN construction method not only guarantees the smoothness of the right side of a dynamic equation, but also maintains the constant level of method complexity; based on the non-polynomial memristor, three scenes of memristor HNN are constructed, and rich dynamic behaviors are found from the HNN, including controllable one-way expansion multi-vortex attractor, controllable grid multi-vortex attractor, chaos coexistence caused by initial offset and periodic coexistence caused by initial offset. The adaptive synchronization method constructs an adaptive state observer and a synchronization controller, selects any two scenes of the memristor HNN as a master system and a slave system in the three scenes of the memristor HNN, adopts the synchronization controller to synchronize the master system and the slave system based on the adaptive state observer, and can well simulate the synchronization between different regions of the brain.

The application discloses a mapping method based on double discrete memristor, and relates to the technical field of memristor, two new double-memristor hyperchaotic mapping models of isomorphic sine-sine discrete memristor SSDM mapping and heterogeneous sine-cosine discrete memristor SCDM mapping are constructed by using two periodic discrete memristors; the mapping has a plane fixed point, and stability distribution is divided according to the second characteristic root in the parameter and initial value plane; bifurcation behavior depending on coupling strength, coexistence extreme multistability controlled by the initial value of the memristor are studied by using a bifurcation diagram, Lyapunov exponent, phase diagram and local attraction basin; under the control of the memristor, coexistence infinite heterogeneous attractors are self-replicated along a straight line and a plane; numerical results show that the SSDM and SCDM mappings can not only generate highly complex hyperchaotic attractors, but also exhibit extreme multistability behavior controlled by the initial condition of the memristor distributed on a straight line and a plane.

This invention discloses a multi-scale phase space correlation dimension analysis method for jet reactors under negative pressure conditions. By introducing phase space reconstruction and attractor analysis methods (i.e., the correlation dimension method), the nonlinear dynamic characteristics of the internal flow field of the jet impacting the reactor are systematically studied. Phase space reconstruction technology reconstructs time-series data into trajectories in a high-dimensional phase space using a delayed embedding method, thereby revealing the system's intrinsic dynamic behavior. Attractors, as geometric structures in phase space, reflect the long-term dynamic characteristics of the system; their shape and distribution can provide important clues for understanding the complexity of the flow field. Through phase space reconstruction and attractor analysis, chaotic behavior, fractal characteristics, and the geometric structure of attractors in the flow field can be identified, thus providing theoretical support for optimizing reactor design and operating conditions.

This invention proposes a method and system for fixed-wing UAV habitat planning based on the attraction domain, belonging to the field of UAV control technology. The method includes step S1: acquiring the core state parameters and control parameters during the flight of the fixed-wing UAV; step S2: estimating the UAV's flight attraction domain based on the Lyapunov model, combined with the core state parameters and control parameters, to obtain an attraction domain set; and step S3: generating a reference flight trajectory based on the attraction domain set and landing target data. This invention also proposes a fixed-wing UAV habitat planning system based on the attraction domain. This invention ensures stable convergence throughout the entire UAV habitat landing process from the perspective of nonlinear system stability theory. Simultaneously, through lightweight modeling and simplified solution, it adapts to the onboard computing capabilities of small UAVs, effectively solving the core defects of existing technologies such as weak robustness, lack of stability guarantee, and complex calculations, enabling small fixed-wing UAVs to achieve precise habitat landing in small spaces without taxiing.

A neuromorphic computing system is disclosed in which inputs are embedded onto a continuous manifold realized by a dynamical substrate of interconnected processing elements that update their states in response to events. The substrate converges to attractor states that encode input-dependent representations, from which geometric properties, including metric tensor components and curvature, are derived from physical characteristics such as spike timing, synaptic weights, and conduction delays. Trajectories on the manifold emerge through evolution of the substrate state and follow geodesic-like paths arising from competitive propagation dynamics. Perturbation-based methods estimate curvature by measuring divergence of nearby trajectories. Parameters of the substrate are adaptively modified via activity-dependent plasticity rules, enabling experience-driven reshaping of the manifold geometry. The substrate may comprise spiking neural networks, memristive arrays, photonic processors, or analog dynamical systems.

A method for predicting future behavior for a dynamic system using an artificial intelligence system implemented within a computer hardware system. A predetermined amount of a time series group of data from the dynamic system defining previous behavior of the dynamic system are received at the artificial intelligence system. An attractor is constructed from the time series group of data that defines the previous behavior of the dynamic system using the artificial intelligence system. The attractor models the previous behavior of the dynamic system based on the predetermined amount of the time series group of data of the dynamic system. A prediction horizon for the predetermined amount of the time series group of data is determined with the artificial intelligence system using an attractor dimension of the constructed attractor and a Lyapunov exponent of the constructed attractor. The prediction horizon increases logarithmically as a length of the predetermined amount of the time series group of data from the dynamic system increases linearly. Prediction values of future behavior of the dynamic system are generated with the artificial intelligence system using the constructed attractor and the determined prediction horizon.

The present application belongs to the field of weak harmonic signal detection, and particularly to a weak harmonic detection method based on Chen system. The parameters a, b, c and the initial value (x0, y0, z0) of the system of the Chen system are set, the critical threshold of the control signal of the system is found out by using the dichotomy according to the bifurcation diagram of the system state variable changing with the amplitude f of the control signal, and the input signal weighting factor A is set according to the critical threshold. After the Chen system for signal detection is debugged, the control signal and the weighted input signal are input into the system at the same time, the system equation of the Chen system is solved, the x-z phase diagram of the system, the attractor distribution and the state variable time history diagram are drawn. The change of the fixed point position or the change of the attractor distribution of the system is judged, if the change occurs, the harmonic signal to be detected exists, at this time, the control signal amplitude f is reduced by a step and the system state is recalculated until the fixed point position or the attractor distribution of the system becomes the initial state, and the ratio of the difference of the control signal amplitude and the weighting factor is the amplitude of the harmonic signal to be detected. The signal can be detected without error under the noise of-60dB, and the detection error under the noise of-100dB can also be kept below 0.1%.

The application discloses a new method for diagnosing shape quality of cold continuous rolling based on causal analysis, which comprises the following steps: reconstructing an attractor manifold according to two features in a same system to obtain a state space; reconstructing an attractor manifold again according to a feature time delay; finding a nearest neighbor point and calculating a nearest neighbor estimation; defining a CME score formula, calculating a CME score, judging whether the CME score converges to a constant or not, and if the CME score converges to a constant, it is indicated that a causal relationship exists between the two features. The application of the diagnosing method to diagnosing shape quality of cold continuous rolling can effectively monitor shape quality abnormality.