1385 results about "Optimal design" patented technology

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention relates to the technical field of three-dimensional reconstruction and simulation, and discloses a foam material porous structure three-dimensional reconstruction and simulation system, which comprises a scanning imaging module used for carrying out scanning imaging on a foam material sample to obtain pore image data; the pore boundary recognition module is used for performing pore boundary recognition based on the pore image data to obtain a pore boundary recognition result; the three-dimensional reconstruction module is used for executing three-dimensional reconstruction of bubble evolution physical constraints according to the pore boundary recognition result to obtain a three-dimensional reconstruction model; the coupling simulation module is used for performing grid division and coupling simulation on the three-dimensional reconstruction model to obtain mechanical simulation data; and the parameter solving module is used for carrying out density field optimization and parameter solving based on the mechanical simulation data to obtain optimal gap design parameters, so that seamless connection from simulation analysis to optimization design is realized, the structural design period of the foaming material is shortened, and the design precision and consistency are improved.

Systems and methods for asset life cycle optimization and management are provided. A probabilistic, physics-based, causal method for predicting the evolution of damage and failure time of an aging asset. The method comprises providing a probabilistic, physics-based, causal network, comprising a plurality of random-variable nodes, wherein the nodes represent at least one of: damage initiation time, damage state, damage rate, damage causal factors, observations, human expert knowledge, failure state, and failure time. The method further comprises applying the probabilistic physics-based causal network to an aging asset; predicting the evolution of damage and failure time of the aging asset; and using this knowledge to make optimal design, inspection, maintenance, and operational life cycle decisions for the aging asset.

The invention discloses an optimization design method of a large-model-driven mechanical part, which comprises the following steps of: performing knowledge retrieval according to a design task and a constraint condition, and generating text description of a structure design scheme by fusing the retrieved knowledge through a large-scale language model (LLM); the LLM performs task decomposition and modeling code mapping on the structure design scheme described by the text, and generated modeling codes are directly executed by modeling software to generate a three-dimensional model; the LLM generates a reasoning track and calls a finite element analysis tool set to perform performance analysis on the generated three-dimensional model; and according to optimization requirements and design constraints, the LLM optimizes the three-dimensional model to generate an optimal three-dimensional model. The invention discloses an optimization design system of a large model driven mechanical part. The optimization design system comprises a design scheme generation module, a parametric modeling module, a design scheme verification module and a structure parameter optimization module. The method and the system provided by the invention can save code quantity, reduce manual intervention and improve design efficiency.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention discloses a building facade optimization method and system based on sustainable analysis, and relates to the technical field of data processing. According to the method, parametric modeling is carried out on the external facade of a target building, and a design space is defined; training a machine learning model integrating energy consumption, carbon emission, cost and lighting prediction functions by using historical data; building an optimization problem by taking external facade parameters as decision variables and taking minimization of energy consumption, carbon emission and cost and maximization of lighting as targets, and automatically generating an optimal scheme set by adopting a multi-target optimization algorithm; a decision maker is assisted to select a final scheme through a visual interface, and a corresponding BIM model is output; the method effectively solves the problems that a traditional design method is difficult to coordinate multi-target conflicts, depends on experience and is low in efficiency, and can automatically and intelligently generate an optimal design scheme which is energy-saving, low-carbon, low-cost and good in lighting performance.

The invention relates to the technical field of automobile design, and particularly discloses an automobile wind resistance optimization design method based on a machine learning model, and the method comprises the steps: collecting data of different automobile types, and carrying out the cleaning and standardization processing; constructing a wind resistance prediction model, training the model by using the preprocessed data, and minimizing a prediction error by adjusting model parameters; key design variables are selected, the variation range of the key design variables is determined, and a parameterized model is constructed; selecting sample points in a design variable space, generating a corresponding geometric model, calculating a wind resistance coefficient and establishing a sample point database; dividing sample point data into a training set and a test set, optimizing a machine learning model, searching an optimal solution in combination with an optimization algorithm, and iteratively updating the model by increasing sample points until the precision requirement is met; an optimal design variable combination is determined, an optimized automobile geometric model is generated, CFD simulation verification is carried out, and a design scheme containing detailed parameters and optimization results is output.

The present disclosure relates to a field of a fuel cell test, and in particular, to a method and an apparatus for optimizing design based on performance evaluation of a gas diffusion layer of a fuel cell. The method includes: determining an overall porosity of the gas diffusion layer of the fuel cell according to production requirements, and obtaining a plurality of porosity structures with the overall porosity; obtaining performance evaluation indexes of the gas diffusion layer of the fuel cell, and constructing a performance evaluation system for the gas diffusion layer of the fuel cell; calculating, with reference to evaluation functions and index weight ratios, performance comprehensive scores of the plurality of porosity structures in the performance evaluation system of the gas diffusion layer of the fuel cell; determining an optimal design scheme in the plurality of porosity structures according to the performance comprehensive scores.

The invention discloses an engine turbine shaft sequential optimization design method based on ensemble learning, and the method comprises the steps: carrying out the parametric modeling of an aero-engine turbine shaft through employing a finite element method, and determining a design variable; determining an optimized objective function and constraint conditions based on the turbine shaft structure; constructing a training point set by using the target function and the constraint condition, and establishing an ensemble learning agent model by using the training point set through an ensemble learning algorithm; executing a sequential optimization design process based on the ensemble learning agent model; searching a current optimal sequence design point by utilizing an intelligent algorithm and a sequential updating criterion, and judging whether the sequence design point meets a threshold value requirement or not; if the current optimal sequence design point meets the threshold requirement, outputting the current optimal sequence design point as a design result; otherwise, adding the sequence design points as new training points into the training point set to update the training point set, updating the ensemble learning agent model by using the updated training point set, and iteratively calculating the sequence design points.

The invention provides an ultra-high performance concrete proportion design method based on an intelligent algorithm and functional requirements. The method comprises the following steps: S1, constructing an ultra-high performance concrete performance prediction database; s2, automatically selecting key variables based on function requirements; s3, training and predicting a machine learning model based on function requirements; and S4, optimal design of the ratio of the ultra-high performance concrete. According to the unsupervised anomaly detection method based on the isolation forest, the database is preprocessed, and the ultra-high performance concrete proportion design key variables are selected through the automatic variable selection method, so that rapid mapping from the mixing amount of each component of the ultra-high performance concrete to each function demand index is realized; through the particle swarm optimization (PSO), the ultra-high performance concrete with specific function requirements is subjected to proportioning design, and finally the ultra-high performance concrete proportioning value closest to the function requirement index is obtained, so that the problems of time and labor consumption, low efficiency and resource waste are solved.

The invention belongs to the field of aero-engines and turbines, and provides a quick prediction method, system and device for a two-dimensional viscous compressible flow field of a gas compressor and a medium, and the method comprises the steps: obtaining CFD simulation data of a two-dimensional blade profile of the gas compressor under multiple working conditions, and carrying out the preprocessing to obtain a standardized training data set; on the basis of the data set, constructing a hybrid neural network model which takes a blade profile geometric parameter and an operation condition parameter as input and takes a two-dimensional viscous compressible flow field as output; combining mean square error, gradient loss and physical consistency loss based on RANS equation residual error to construct a total loss function, and performing end-to-end training on the model; during reasoning, to-be-predicted parameters are input into the trained model according to the same preprocessing mode, and then a flow field prediction result can be rapidly output. According to the method, the problems of long consumed time, low precision and poor consistency of traditional CFD simulation can be solved, millisecond-level flow field prediction is achieved, high precision and high physical consistency are achieved, and the method is suitable for efficient optimization design of the blade profile of the gas compressor.

The invention belongs to the field of vehicle industry design, and discloses an electric bicycle form design method and system, electronic equipment and a storage medium, and the method comprises the steps: mining user emotion vocabularies through online comments, constructing an emotion lexicon in combination with an improved word frequency-inverse document frequency algorithm and a D-S evidence theory, and screening out key perceptual vocabularies; constructing a convolutional long-short-term memory neural network model optimized by a snake swarm algorithm to realize a mapping model between customer sensibility and product morphological characteristics; carrying out subjective and objective comprehensive evaluation on the design scheme in combination with an eye movement experiment and subjective evaluation so as to screen out an optimal design scheme; an optimal scheme is selected and input into a generative AI platform for multi-angle visual rendering, ergonomic modeling and aerodynamics simulation are assisted, and the structural feasibility and performance are verified. The method provides systematic technical support for emotional value improvement and design optimization of industrial products.

The invention particularly relates to an aircraft accompanying optimization design method based on a low-dissipation format, and the method comprises the following steps: 1, giving an initial shape of an aircraft needing to be optimized, and generating a CFD grid of the initial shape; parameterizing the aircraft based on the initial shape to obtain a design variable; 2, setting an optimization target and constraint conditions, and establishing an optimization problem mathematical model; 3, obtaining a high-precision convergence flow field solution calculated by adopting a low-dissipation AUSMPWP format, and obtaining an aerodynamic objective function value of the optimized mathematical model; a discrete adjoint equation and a sensitivity equation are constructed and solved on the basis of a low-dissipation AUSMPWP format, and the gradient, about design variables, of a target function in the optimized mathematical model is obtained; and 4, based on the aerodynamic objective function value and the gradient information obtained in the step 3, performing optimization solution on the optimization problem in the step 2 by adopting a sequential quadratic programming optimization algorithm to realize low-dissipation accompanying optimization design of the aircraft.

The invention discloses a bipolar plate structure optimization method and system based on flow field data analysis, and relates to the technical field of fuel cells. The method comprises the following steps: establishing a parameterized bipolar plate runner geometric solid model; performing compression deformation simulation of a gas diffusion layer on the bipolar plate flow channel geometric solid model to obtain physical field data of the gas diffusion layer, and calculating a porosity distribution field and a local mass transfer impedance coefficient field based on the physical field data; based on the porosity distribution field and the local mass transfer impedance coefficient field, multi-physics field coupling simulation is carried out, and a local performance contribution degree field is obtained; constructing a comprehensive loss function according to the local mass transfer impedance coefficient field and the local performance contribution degree field; and with the purpose of minimizing the comprehensive loss function, iterative optimization is carried out on the design variables through an optimization algorithm until convergence conditions are met, and an optimal design variable combination is output. Through parametric modeling, multi-physics field coupling simulation and comprehensive loss function evaluation, efficient optimization of the bipolar plate design is realized.

The invention discloses a method and a system for designing a fluid flow channel of a calcium-based thermochemical heat storage fixed bed reactor based on topological optimization and a storage medium. The method comprises the following steps: S1, constructing a geometric model of a design domain of the reactor; s2, constructing an objective function and establishing a two-dimensional topological optimization mathematical model based on a control equation of conjugate heat transfer and fluid flow; s3, solving the mathematical model to obtain an optimal fluid flow channel two-dimensional topological structure; s4, performing geometric reconstruction on the two-dimensional topological structure obtained in the step S3, converting the two-dimensional topological structure into a Bezier curve, and integrating the Bezier curve into a geometric model corresponding to the original design domain; s5, establishing a finite element model of the reactor; s6, temperature field and pressure field distribution is calculated based on CFD simulation, whether the heat storage performance target is met or not is judged, and if not, corresponding control parameters are adjusted to return to the step S3 until the heat storage performance target is met; the device can optimize the heat transfer and mass transfer performance of the reactor at the same time, and the optimization design efficiency is high.

The invention discloses an optimization design method for improving the environmental performance of a building site, and relates to the technical field of site design. The method comprises the following steps: S1, collecting and preprocessing basic information of a building site; s2, building a multi-dimensional digital model of the building site; s3, site environment performance simulation and index quantification are carried out; s4, environment performance parameters are intelligently optimized; s5, verifying and finally determining an optimization scheme; and S6, optimizing effect tracking and dynamic adjustment. According to the method, adjustable variables of a building candidate model serve as genes, binary crossover and polynomial variation are simulated through iteration of 100-200 sets of initial populations, and a multivariable combination space is automatically traversed; according to the method, planning specifications and a cost threshold value are converted into algorithm constraints, it is ensured that an output scheme is compliant and economical, 3-5 groups of non-inferior solution schemes are screened out in 3-5 rounds of iteration, standardized values of environmental performance parameters after optimization are all larger than or equal to 0.7, local optimum is avoided, and optimization efficiency and scheme comprehensiveness are greatly improved.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention relates to the technical field of flue gas pollutant control, and discloses an SCR denitration reactor optimization method based on CFD flow field simulation, and the method comprises the following steps: S1, building an initial CFD model, carrying out reference simulation, and obtaining the performance data of the clean state of a reactor; s2, constructing an ash deposition dynamic evolution model, and performing flow field-ash deposition-geometric coupling simulation in combination with a dynamic grid technology to obtain time-varying operation data; s3, defining and quantifying a time domain performance index of the reactor in a preset period by using the time-varying operation data; and S4, optimizing the design variables by adopting a multi-objective optimization algorithm based on an agent model and taking the time domain performance index as an objective to obtain an optimal design scheme. Through dust deposition dynamic coupling simulation and agent model multi-objective optimization, the long-term operation stability and the comprehensive denitration performance of the SCR reactor under the actual dust deposition condition are remarkably improved, and the optimization design efficiency is greatly improved.

The invention discloses a deep-sea mining VTS configuration optimization design method based on data driving, and belongs to the technical field of deep-sea mining engineering and optimizing.The method comprises the steps that firstly, a mechanical response model of a VTS is constructed through a VFIFE method, and mechanical response is calculated in combination with VTS structure parameters and borne external force; then generating a prediction agent model based on CPO-BP, and realizing relation modeling between the configuration design variable and the mechanical response of the VTS; and finally, the prediction agent model is embedded into the MODA-DE, the VTS configuration is optimized based on the MODA-DE, and VTS configuration design variables meeting the multi-objective optimization requirements are finally obtained through multi-generation iteration. According to the scheme, the three links of VFIFE, CPO-BP and MODA-DE are closely combined, an efficient multi-target optimization technology is formed, collaborative improvement from high-precision prediction to global optimization is achieved in mining pipeline configuration design, and the method has high actual popularization and application value.

The invention provides a tensor segmentation and mapping method of a neural network operator on a wafer chip, and the method comprises the steps: expanding a forward calculation graph into a training calculation graph containing forward propagation, back propagation and gradient updating, and employing different operators in the calculation of these stages; carrying out dimension segmentation on the input tensor of each operator in the training calculation graph to generate a plurality of global candidate segmentation strategies; aiming at each candidate segmentation strategy, enumerating feasible resource mapping strategies of all operators, and combining to generate candidate design points; and screening an optimal design point through cost evaluation, and outputting a corresponding candidate segmentation strategy and a resource mapping strategy. According to the method, forward and backward operators in a neural network training process are decoupled through expansion of a calculation graph, and a segmentation scheme for avoiding tensor copying in forward and backward calculation in the training process and a resource mapping strategy corresponding to the segmentation scheme are explored, so that tensor copying is avoided, global resource occupation is reduced, and the training efficiency is improved. Therefore, the same wafer-level chip can support larger model training.

The invention discloses a hypergraph neural network-based PVA fiber toughened UHPC material formula design method. The method comprises the steps of 1, multi-modal data acquisition and processing, 2, hypergraph structure construction, 3, HGNN model construction, 4, model training, and 5, performance optimization and reverse ratio design. According to the method, the high-order relationship among the multi-modal data such as the raw material, the proportion and the microstructure can be efficiently modeled, the precise prediction of the performance of the PVA fiber toughened UHPC and the optimal design of the proportion are realized, and the method is suitable for the performance prediction and proportion recommendation of various ultra-high performance concrete systems, and has wide engineering application prospects and industrial popularization values.

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention discloses a comprehensive support hanger section multi-objective optimization method based on NSGA-II and an optimal determination method. The comprehensive support hanger section multi-objective optimization method comprises the following four steps: establishing a multi-objective optimization mathematical model of a comprehensive support hanger steel section; establishing a non-dominated sorting genetic algorithm; the NSGA-II is adopted to solve the multi-objective optimization mathematical model; establishing implementation of a multi-objective decision-making method; evaluating, sorting and selecting solutions in the approximate Pareto non-inferior solution set by adopting a multi-objective decision-making method; according to the optimized design scheme, final design parameters of the comprehensive support hanger steel section are determined. The method is directed to comprehensive supports and hangers used in construction machinery, electrical and piping systems, aiming at simultaneously achieving material cost minimization and bending performance maximization. According to the method, the defects in the aspects of multi-objective optimization and comprehensive decision making in the prior art are overcome, and an efficient and practical solution with multi-objective comprehensive performance is provided for the design of the steel section of the comprehensive support hanger.

The invention provides a tidal current energy water turbine intelligent design method based on a generator and a predictor and a water turbine structure optimization design framework. According to the method, a compact generator is obtained by training a variational self-encoding generative adversarial network and is used for parametric modeling of tidal current energy water turbine rotor blades, and an efficient performance predictor based on the combination of an artificial neural network and transfer learning is established to realize intelligent prediction of blade performance. The generator and the predictor are coupled with the multi-target genetic algorithm, efficient design of the tidal current energy water turbine blade is achieved, the performance of the tidal current energy water turbine blade can be rapidly predicted, and then efficient optimization design of the tidal current energy water turbine is achieved.

The invention discloses a cargo floor structural strength design optimization method and system based on load prediction, and belongs to the technical field of freight vehicle structural design. The maximum equivalent stress of each node in a floor grid area is calculated and normalized by constructing a load prediction model of the dynamic distribution state of cargoes in a cargo tank; forming a stress intensity coefficient matrix; identifying a high-risk area and constructing a reinforced target area set; obtaining an initial parameter of the bottom plate structure, establishing a finite element model, and setting an adjustable structure parameter in the target area; a candidate structure design scheme set is generated, simulation calculation is executed, the total structure mass, the maximum stress value and the first-order inherent frequency of each scheme are extracted, and a performance evaluation vector is formed; selecting an optimal design scheme through a multi-objective optimization algorithm, and outputting structural parameters of the optimal design scheme as a final design result; according to the method, the structural optimization design of the cargo tank bottom plate under the complex load working condition is achieved, and the method has high engineering adaptability and practical value.

The invention provides a circuit optimization method, system and device based on model predictive control and a medium, and relates to the technical field of integrated circuit design, and the method comprises the steps: constructing a circuit knowledge graph and a simulation data lookup table according to a circuit design experience document and circuit simulation data; initializing a search feasible region of design points according to the circuit knowledge graph and the simulation data lookup table, and performing multi-point exploration Bayesian optimization to obtain a candidate design point set of the current circuit; and according to the simulation data lookup table, the circuit knowledge graph and the candidate design point set, in combination with a model prediction control framework, executing a reinforcement learning method to obtain an optimal design point of the current circuit, and providing optimal configuration of the current circuit. According to the method, the consumption of simulation computing resources is effectively reduced, the optimization efficiency is improved, and meanwhile, the design is ensured to meet the multi-target performance requirements of gain, power consumption, bandwidth and the like.

The invention belongs to the field of intelligent manufacturing and engineering design, and particularly relates to an industrial fan structure optimization design method and system based on AI assistance. According to the method, structural components of an industrial fan are modeled into a multi-node topological structure, a dual-channel performance prediction model is constructed, and the mapping relation between the structure and the performance is accurately established. On the basis, a comprehensive objective function is constructed, a boundary penalty mechanism is introduced, a virtual input parameter space is constructed in combination with a structure disturbance sample, a strategy gradient optimization method is adopted to carry out structure search and iterative optimization, and finally a structure scheme meeting boundary conditions and optimal performance is screened out through simulation verification. The system module comprises structure modeling, model training, objective function construction, disturbance generation, optimization execution, simulation verification and result output, and has the advantages of high structure identification precision, high optimization efficiency, strong engineering adaptability and the like.

The invention relates to a multi-target aerodynamic-structure optimization method based on a deep neural network proxy model, and solves the problem that an existing method is insufficient in efficiency, feasibility and credibility, and the method comprises the steps: firstly, carrying out the sampling in a parameterized design target, building an aerodynamic-structure database through CFD / FEA high-fidelity simulation; the method comprises the steps that firstly, a multi-task neural network is used for predicting the lift-drag ratio, quality and stress at the same time, uncertainty is output, then, in NSGA-III multi-target optimization, a proxy model is used for rapidly evaluating and searching out a Pareto leading edge meeting constraints, finally, the precision of the model is gradually improved through high-fidelity checking and incremental updating, and an optimal design scheme is output. According to the method, sub-optimization and repeated iteration caused by'pneumatic first and then structure 'are avoided; the design universality and robustness are improved; a compromise scheme set of the system is directly obtained; and the optimization cost and the optimization period can be obviously reduced.

The invention belongs to the field of filter optimization of coupling matrixes and neural networks, and discloses a filter optimization design method of a neural network based on guidance of a coupling matrix, and physical elements and mechanisms are introduced into the neural network. The method comprises the following steps: firstly, determining filter optimization indexes, randomly generating sample data in a range near the size of the optimization indexes, and screening data samples by adopting a sample screening method to obtain high-quality samples; then, extracting a coupling matrix from the obtained S parameter according to a vector fitting algorithm, and forming new coupling information M'by combining the center frequency f0 and the bandwidth BW; a full-connection neural network is used as a medium of filter device size parameters and output frequency response, the size of a to-be-optimized device of the filter is input, and coupling information M'representing S parameters is output. And the forward simulator is connected with a genetic optimization algorithm GA to realize optimization design of a specific index parameter filter. According to the method, the optimization design time is effectively saved, the optimization efficiency is improved, and the optimization dimension is reduced.