15 results about "Human learning" patented technology

A long-term continuous learning method based on task core memory management and consolidation aims to enable a model to sequentially learn from a large number of task sequences, new knowledge is obtained, information of previous learning is reserved, and the method is similar to a human learning mode. The method comprises the following steps: 1) task input and instruction fine tuning; 2) performing difference analysis on the model parameters of the current task and the previous task, identifying a task core memory unit, calculating an adaptive weight based on task prototype similarity, and dynamically updating the memory unit; 3) constructing an experience playback buffer area through a difficult sample selection strategy and a difference sample selection strategy; and 4) utilizing the joint loss function training model to keep the memory of the historical tasks while learning the new tasks. According to the method, the problem of disastrous forgetting in long-term continuous learning is mainly solved, and the performance of the model in a long-term sequence task is remarkably improved.

The application discloses a permanent magnet synchronous motor stator fault diagnosis method and system based on a human learning optimization algorithm, and the method comprises the following steps: obtaining a three-phase current signal of a stator of a permanent magnet synchronous motor, converting the signal into a two-dimensional time-frequency diagram after being decomposed by a variational mode and being demodulated by a Hilbert envelope; constructing a binary neural network for processing the two-dimensional time-frequency diagram and determining a search space; generating a candidate architecture by global search using a first human learning optimization algorithm; for each candidate architecture, fine-tuning the classification layer parameters thereof by proportional evolution using a second human learning optimization algorithm, taking the classification performance of a verification set as a fitness value, and iteratively obtaining an optimal model; inputting a current signal to be diagnosed into the model after the same preprocessing, and outputting a stator fault diagnosis result. Compared with the prior art, the application realizes low-overhead accurate diagnosis of weak faults of a permanent magnet synchronous motor stator by physical information driven preprocessing and double-layer human learning optimization.

1. Name of the product in this design: Desk (6120 extension). 2. Purpose of this design: For human learning purposes. 3. The key design feature of this product is its shape. 4. The picture or photo that best illustrates the key design points: Design 1 3D diagram 1. 5. Design 1 is designated as the basic design.

1. Name of the product in this design: Desk (5672). 2. Purpose of this design: For human learning purposes. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The invention discloses a large language model training method, device and equipment applied to a professional field and a storage medium, and the method comprises the steps: obtaining a related document of a current field, and carrying out the chapter splitting of the related document, and obtaining a plurality of text blocks; generating a plurality of structured question and answer pairs based on each text block, wherein each structured question and answer pair comprises questions and answers corresponding to the document chapters to which each text block belongs; and inputting each text block and each structured question and answer pair into a basic language model, and performing iterative training on the basic language model according to a preset progressive fine tuning strategy to obtain a target large language model. The domain document can be subdivided according to chapters and sections, structured question and answer training is performed on each text block, and then the model is trained by simulating a human learning process through the preset progressive fine tuning strategy, so that terminologies and concepts can be integrated step by step and iteratively; therefore, the knowledge adaptability of the model in the specific field is remarkably improved.

The application discloses a kind of fusion course learning strategy's pulse neural network optimization method, including based on the training sample in training set to the initialized pulse neural network is trained, obtains prediction result;The prediction result is compared with the sample label in training set, and confidence loss is calculated;With confidence loss minimum as objective function, layer by layer forward derivation is carried out, and the parameter of each neuron in pulse neural network is updated;The parameter of optimization pulse neural network is constantly updated, until the iteration of the set round is completed.The application introduces course learning in pulse neural network, dynamically evaluates the difficulty of sample in the training process, expands the contribution of relatively simple sample in back propagation to the current state of model, and reduces the influence on parameter update for the current relatively difficult sample.The pulse neural network optimization strategy has high biological rationality, effectively realizes the process of simulating human learning new knowledge in pulse neural network.

1. Name of the product in this design: Study Desk (A13XT3406). 2. Purpose of this design: For human learning purposes. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application provides a kind of mathematical reasoning data synthesis method and system based on agent simulation teaching scene, it is related to big language model technical field, method includes: obtaining public thought chain data set, the big language model to be promoted mathematical reasoning ability is reasoned on thought chain data set, and the error rate of big language model on each question is obtained;Create multi-role agent, use teacher agent to analyze the problem of big language model analysis error;Based on the expected reasoning data amount of each question of error rate and preset data set size;Use multi-role agent to simulate multiple teaching scenarios, and convert the dialogue data in multiple teaching scenarios into the same data as the thought chain data set format, and synthesize the mathematical reasoning data set of the expected reasoning data amount.The application effectively solves the problems of high construction cost, low quality density and serious data homogenization in the current analogy human learning reasoning data synthesis method.

1. Name of the product in this design: Storage Desk (T07XX4803). 2. Purpose of this design: For human learning purposes. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D rendering 1.

The present invention discloses a green dynamic multi-objective scheduling method for a flexible assembly workshop under the human learning effect, and relates to the technical field of workshop scheduling. Based on the green dynamic multi-objective scheduling requirements of a flexible assembly workshop under the human learning effect, the present invention establishes a multi-objective dual-agent planning model. Under the dual resource constraints of machines and personnel, the model's dynamic multi-objective scheduling problem is solved based on a two-layer deep reinforcement learning framework method. The state space and action space are designed based on the workshop simulation environment, and a reward function combining immediate rewards and round rewards is combined. The agent interacts with the scheduling environment to obtain a more optimal scheduling rule at the scheduling point. Through the above method, the present invention improves the decision-making efficiency of manufacturing enterprises, can adaptively and quickly generate a more optimal solution, effectively reduce losses caused by delay time, and reduce energy consumption, while also having certain generalization and stability.

This invention discloses a cross-scenario autonomous driving decision-making method based on adaptive perception. This invention effectively transforms complex environmental information into basic scenarios and makes decisions based on these scenarios. Compared to traditional autonomous driving technologies, this invention shows significant advantages in complex traffic environments. When facing unfamiliar scenarios, this invention can utilize existing training experience and transformed perception images, reducing reliance on large amounts of data and long training times. By simulating the human learning and driving process, this invention enables the autonomous driving system to make accurate decisions using existing training knowledge in unfamiliar scenarios, thus avoiding dependence on large amounts of training data and long training times. Through this adaptive perception mechanism, it can also process and transform bird's-eye view images of varying complexity, providing more easily understood input to the decision-making module, resulting in a more efficient and adaptable autonomous driving solution.

The application relates to a four-legged robot motion control method based on a guided human learning optimization algorithm, which comprises the following steps: initializing a real number population of a continuous human learning optimization algorithm; controlling a four-legged robot to collect state transition data through a lower-layer proximal policy optimization algorithm; decoding a current candidate individual into a shaping reward weight value, constructing a total reward function with a fixed weight basic reward, and calculating an instant reward value; updating neural network weight parameters; after a preset gradient update period, collecting simulation trajectory data and calculating a comprehensive motion performance fitness value, and feeding back to an upper-layer algorithm; the algorithm updates a knowledge base and generates a next-generation candidate individual; whether a maximum iteration number is reached is judged, if not, iteration is continued, and if yes, an optimal shaping reward weight combination and corresponding neural network weight parameters are output. Compared with the prior art, the application has the advantages of high robustness, strong self-adaptation and good convergence.

The invention discloses an optimization learning method based on a memory model, and the method comprises the steps: constructing a long memory learning model based on fractional differential; constructing a short memory model based on the long memory learning model and the attenuation characteristics; discretizing the short memory model to obtain a discrete memory model; and based on a particle swarm optimization algorithm and the discrete memory model, optimizing the memory parameters, and obtaining a memory optimization model. According to the method and the device, the memory learning of people is expanded to machine learning on the basis of memory characteristics based on the learning of people, so that the machine learning has better convergence and better learning speed at the same time. According to the method, the historical information is weighted according to the memory characteristics by utilizing the memory attenuation characteristics, the weight of the historical information exceeding a certain length is relatively small and can be ignored, and the learning speed and the learning efficiency are both considered.

1. Name of the product in this design: Desk with cabinet (X18XX3000). 2. Purpose of this design: For human learning purposes. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.