6 results about "Evolutionary computation" patented technology

This invention relates to the field of neural network architecture search and industrial intelligent processing technology, and discloses a structure search method for multi-output dendritic neuron models for industrial classification tasks. The method includes: initializing the synaptic connection weights and dendritic threshold parameters of the multi-input multi-output dendritic neuron model to generate an initial population; uniformly dividing the initial population into several subpopulations of equal size; calculating the temporal and spatial criteria for each subpopulation after task allocation and optimization, and dynamically allocating evolutionary computational resources for each subpopulation based on the fitness change rate represented by the temporal criterion and the population distribution state represented by the spatial criterion; implementing an accelerated sharing penalty mechanism to adjust fitness values ​​according to the crowding degree among individuals to maintain solution set diversity; and updating each subpopulation. This invention solves the problem of low convergence efficiency caused by uniform resource allocation in traditional large-scale multi-objective evolutionary algorithms during neural network architecture search.

The application discloses a kind of attention mechanism LSTM fan blade fault diagnosis method, device, electronic equipment and medium based on differential evolution optimization.The method can include: collecting SCADA data, determining feature dataset;LSTM neural network is trained by feature dataset, and output state is obtained;Output state is input to attention mechanism model, and output result is obtained;Output result is obtained by output module to obtain prediction result, and prediction result is compared with fan actual state, and output error is calculated;According to output error, differential evolution calculation is carried out, and the parameters of LSTM neural network and attention mechanism network model are updated;Fan data to be tested is input into the model after training, and the diagnosis result of fan blade fault is obtained.The application has higher comprehensive performance and generalization ability, and has the advantages of improving the accuracy and speed of fan blade fault diagnosis.

The present application relates to the medical health information technology field, and discloses a personalized anti-inflammatory diet recipe recommendation system and method based on artificial intelligence; the present application constructs a user health portrait through collecting multi-source data such as wearable devices, electronic health records, gene sequencing, combines a dynamically updated anti-inflammatory knowledge graph, evaluates the user's quantitative inflammation load index and nutrient intervention target by using multi-modal deep learning based on the attention mechanism, adopts a hybrid intelligent optimization algorithm combining constraint satisfaction and evolutionary computing to generate the Pareto optimal anti-inflammatory recipe under the constraints of taste taboo, cost, cooking time and the like, and optimizes the model online through user physiological and subjective feedback; personalized, precise and dynamically adaptive anti-inflammatory diet recommendation is realized, and the scientificity and compliance are improved from group guidelines to individual intervention.

The application discloses an evolutionary computing-based dynamic path multi-AGV charging pile site selection optimization method and system. The method comprises the following steps: determining a plurality of workstation positions as candidate charging pile arrangement points, and representing whether each position is arranged with a charging pile as a binary decision variable; based on a multi-objective optimization model, taking the minimization of the number of charging piles, the maximization of charging pile coverage and utilization as the target, under the constraint conditions of meeting at least one charging pile arrangement, energy accessibility and full coverage, an evolutionary algorithm is used to iteratively optimize the initial population, and a charging pile layout scheme suitable for multiple sets of AGV operation routes is generated; through multi-scenario energy constraint verification and a self-adaptive repair mechanism, it is ensured that the layout scheme still meets the AGV charging demand under the route change; finally, the scheme quality is further improved through local optimization and simulated annealing strategy. The application can realize the comprehensive optimization of the economy, coverage balance and system robustness of the charging pile layout.

This invention belongs to the field of reservoir group scheduling technology, specifically providing a method and system for optimizing the scheduling of cascade reservoirs based on the synergy of physical embedded deep learning and evolutionary computation. This invention hard-codes physical rules into a temporal convolutional network, constructing a physical embedded temporal convolutional network surrogate model. Through survival analysis, the discrete water discharge objective is transformed into a differentiable loss. PeTCN is used to perform gradient inverse optimization, generating a high-quality initial solution for outflow that satisfies the constraints. This initial solution is injected into the NSGA-III algorithm population for hot start, overcoming the feasibility obstacles caused by random initialization, accelerating the Pareto front search, and ultimately obtaining a non-dominated solution set that balances power generation benefits, water discharge control, and delayed water discharge. This invention structurally ensures the satisfaction of physical constraints, significantly improves the efficiency of feasible solution generation and optimization convergence speed, and the obtained solution set is uniformly distributed and highly applicable to engineering, effectively supporting the efficient and safe operation and scheduling of cascade hydropower stations.

This invention relates to the technical field of wind power prediction, and more specifically, to a short-term wind power prediction method for newly built wind farms with limited data. More specifically, it is a short-term wind power prediction method for newly built wind farms with limited data based on evolutionary generative adversarial networks (GANs) and bidirectional gated recurrent units (GRUs). This invention employs evolutionary computation to optimize the GAN, enabling the generative model to efficiently learn the marginal distribution of the original limited data and generate new data with modal diversity and similar marginal distributions. This compensates for the limitations of the original small-scale data, practically improving the accuracy of wind power prediction for newly built wind farms with limited data. Furthermore, it uses a cross-multiplexing optimization algorithm to optimize the weights and biases of the Dense layer in the BiGRU network, effectively avoiding the model getting trapped in local optima and helping it find the global optimum, significantly improving the accuracy of wind power prediction for newly built wind farms with limited data.