Math toolbox

Description

Machine Learning Toolbox provides regression analysis, classification analysis, cluster analysis functions, support for principal component analysis, factor analysis, Platts analysis, non-negative matrix decomposition, design of experiments (DOE), statistical process control, survival time data analysis. Using the machine learning toolbox can be high-performance, accurate data machine learning analysis.

Features

• Regression Analysis Fitting regression-like models based on sample data, including generalized, linear, nonlinear, multivariate, polynomial, and linear mixed effects.
• Classification Analysis Fitting classification models based on sample data, including support vector machines (SVM), plain Bayesian, discriminant analysis, tree model, k-nearest neighbor, and semi-supervised classifiers.
• Cluster Analysis Fitting clustering models based on sample data, including hierarchical clustering, Hidden Markov Models, k-means, spectral clustering and tree models.
• Dimensionality reduction Conducting PCA Principal Component Analysis (PCA), Factor Analysis, Prandtl Analysis, and Non-negative Matrix Decomposition based on sample data.
• Industrial Statistics Conducting Design of Experiments (DOE), Statistical Process Control, and Survival Time Data Analysis based on sample data.

Description

Deep Learning Toolbox provides functions for building, training and saving deep learning models, including image deep learning, time series, sequence and text deep learning, pre-training network, network training components, function approximation and clustering and other functional modules. Deep Learning Toolbox can be used for pattern recognition, regression prediction, mechanistic data fusion modeling and so on.

Features

• Image deep learning Provides the construction function of various network layers related to convolutional neural network, including convolutional layer, pooling layer, normalization layer, activation layer and other network layers, and provides image data preprocessing and neural network visualization functions.
• Timing, Sequence and Text Deep Learning Provides the construction function of various network layers related to recurrent neural networks, including LSTM, bidirectional LSTM, GRU and other network layers.
• Pre-training network Provides a variety of pre-trained network models based on ImageNet dataset training, including GoogleNet, Inceptionv3, Xception and so on.
• Network Training Components Provides neural network training and training parameter setting functions, including training process visualization, training process printing, training rounds setting, learning rate setting, etc.
• Function Approximation and Clustering Provides regression, classification and clustering functions using shallow neural networks, including the creation of neural networks to summarize non-linear relationships between example inputs and outputs, and unsupervised feature learning using self-coder neural networks.

Product Description

DSP system toolbox for modern digital signal processing system design and simulation to provide a variety of commonly used algorithms, components and applications, to provide signal generation, signal processing and analysis, filter design and analysis, filter implementation, signal conversion and spectral analysis, signal statistics and linear algebra operations and other functions. The DSP system toolbox can be used for digital signal processing, filter design and analysis, as well as for the simulation and analysis of digital signal processing algorithms.

Product Features

• Digital Signal Processing Framework Individually designed overlay data, function specifications for data indexing, buffering, and algorithmic task execution flow frameworks for dynamic simulation of serial and parallel processing of digital systems.
• Dedicated algorithm library for dynamic simulation of digital signal processing. Includes functions and components for signal generation, analysis, transformation, and filter implementation.
• Filter Design Enables a new specification-object-based filter design methodology, supporting more types and parameter input modes of filter design functions.

Product Description

DSP system toolbox for modern digital signal processing system design and simulation to provide a variety of commonly used algorithms, components and applications, to provide signal generation, signal processing and analysis, filter design and analysis, filter implementation, signal conversion and spectral analysis, signal statistics and linear algebra operations and other functions. The DSP system toolbox can be used for digital signal processing, filter design and analysis, as well as for the simulation and analysis of digital signal processing algorithms.

Product Features

• Digital Signal Processing Framework Individually designed overlay data, function specifications for data indexing, buffering, and algorithmic task execution flow frameworks for dynamic simulation of serial and parallel processing of digital systems.
• Dedicated algorithm library for dynamic simulation of digital signal processing. Includes functions and components for signal generation, analysis, transformation, and filter implementation.
• Filter Design Enables a new specification-object-based filter design methodology, supporting more types and parameter input modes of filter design functions.

Product Description

Control Systems Tools is aimed at both classical and modern control domains, providing algorithms, models, and applications for analyzing, designing, and regulating linear control systems. The toolkit supports the specification of control system components as transfer function, state space, or zero-pole gain models, and the functions and applications enable the analysis and visualization of the system response in the time and frequency domains. The control system tools also provide support for Modelica-based analysis of physical controlled object models, control law design and regulation.

Product Features

• Linear Modeling Create linear models of control systems using transfer functions, state spaces, and other representations, and implement conversions between models of different forms, continuous-discrete conversions of models, and more.
• Transfer function and state space modeling Create linear time-invariant system models using transfer function or state space representations, and support the construction of complex system models through series, parallel, and feedback connections of basic models.
• Model Formal Transformation and Continuous Discrete Transformation Supports interconversion between different representations of transfer function, state space, and zero-pole gain models, and converts models between continuous and discrete time domains.
• Linear Analysis View and compare time and frequency domain responses between multiple models, checking characteristics such as rise time, stabilization time, and maximum overshoot, using step response, impulse response, Porter's plot, Nichols' plot, Nyquist's plot, singular value distribution, and zero-pole distribution plots.
• PID Control Create various forms of PID controllers, form them into closed-loop feedback control loops with the model of the object to be controlled, and verify control effectiveness.
• Interactive Compensator Design Graphically design and tune general-purpose control components such as PID, overdrive/lag networks, etc. using root trajectory diagrams, Bode diagrams, nichols diagrams, etc.
• State estimation and LQG design Support for designing observers, including linear and nonlinear Kalman filters, etc., using state-space control design methods such as LQG/LQR and pole configuration algorithms.
• Control Analysis and Design in Sysplorer For the physical model of the controlled object constructed by Modelica, use the Linearization/Estimation APP to linearize it or estimate the frequency response characteristics to support the next step of control design.

Description

Static Code Inspection Toolkit supports specification checking based on C and C++ coding rules, detecting code defects, checking for design errors, and giving hints for modification. Using static code checking toolkit can improve software code standardization and reduce bugs. Product Features

• Supports code specification checking for C\C++ code. Supports code specification checking for C89\C99\C11\MISRA-C2012, and supports code specification checking for C++03\C++14\C++11\C++20.
• Supports checking for code runtime errors Provide design error checking other than compiler error, such as divide-by-zero checking, dead code checking, integer overflow checking, array out-of-bounds checking, data type mismatch and so on.
• Provides error localization functions Supports quick code analysis, outputting error entries, causes of errors, the file where the error occurs, the code line and column where the error occurs, and supports highlighting the location of the error.

Product Description

Static Code Inspection Toolkit supports specification checking based on C and C++ coding rules, detecting code defects, checking for design errors, and giving hints for modification. Using static code checking toolkit can improve software code standardization and reduce bugs.

Product Features

• Supports code specification checking for C\C++ code. Supports code specification checking for C89\C99\C11\MISRA-C2012, and supports code specification checking for C++03\C++14\C++11\C++20.
• Supports checking for code runtime errors Provide design error checking other than compiler error, such as divide-by-zero checking, dead code checking, integer overflow checking, array out-of-bounds checking, data type mismatch and so on.
• Provides error localization function Supports quick code analysis, outputting error entries, causes of errors, the file where the error occurs, the code line and column where the error occurs, and supports highlighting the location of the error.

Product Description

Real-Time Simulation Toolbox is a software tool specialized in hardware-in-the-loop simulation, which provides driver board IO model library, host computer software, real-time simulation engine deployed in the lower computer, supports deploying the model generated code established by MWORKS.Sysplorer to the real-time simulator, implanting the real-time simulation engine into the target real-time operating system, and introducing and outputting signals through the IO boards and connecting with the electronic controllers to be tested. The IO board introduces and outputs signals and connects them to the electronic controller to be tested. The real-time simulation toolbox can be used to build a complete set of hardware-in-the-loop simulation environments for the verification of electronic controllers for users in the aerospace, automotive, marine, home appliance, industrial control and other industries.

Product Features

• Supports real-time simulation and remote deployment Provide real-time simulation engine adapted to a variety of lower computer real-time operating system, support the upper computer to realize the lower computer cross-compile, support the user to control the real-time simulation process.
• Provides the upper computer to support user operation and visualization Provide visualization configuration interface, users design the upper computer software according to the application validation requirements, and connect the components with input and output ports, state quantities, calibration parameters, and support the real-time dynamic display and observation of data, online parameter adjustment, and data logging.
• Support customized development Support customized design and development services of IO board driver module; support customized development of business-oriented system solutions; support adaptation with various real-time operating systems and driver boards.

Description

Model Test Design Toolbox supports model parameter test design and system model test design, and simulates and analyzes the factors affecting the system based on test algorithms such as full analytic factor design, Latin hypercube design, uniform design and optimal D design. With the model test design toolbox, you can carry out the test design and verification of system selection of product architecture model, and the test design and verification of product system parameters.

Product Functions

• Experiment design algorithm Provide test algorithms such as full analytic factorization design, Latin hypercube design, uniform design, D-optimal design, etc. to meet the needs of users in different application scenarios.
• Save and import configuration Support saving the current experimental design scheme and system model as configuration files, and quickly reproducing the design scheme and system model by using the function of importing configuration.
• System Selection Function Support the design verification of multi-systems and multi-schemes, and support the display of different combinations of simulation results, such as the simulation verification of multi-body schemes and multi-motor design schemes.

Product Description

Parameter Sensitivity Analysis Toolbox provides design of experiments algorithms for designing different parameter combinations and algorithms such as Finite Difference Method (FDM) and Global Sensitivity Analysis (GSA), which support global and local sensitivity analysis of the system. Using Parameter Sensitivity Analysis Toolbox, local/global sensitivity analysis of product system model parameters can be performed to identify key parameters.

Product Features

• Automatically add sweeps and perturbations The Sensitivity Analysis Toolbox supports the automatic generation of sweeps and perturbations with different combinations of parameters to analyze parameter sensitivity.
• Parameter Sensitivity Analysis Sensitivity Analysis Toolbox supports global and local sensitivity analysis of the system. Output the results according to the influence of each parameter on the system performance.
• Real-time data display The analyzed data can be dynamically displayed and integrated into MWORKS.Sysplorer. Sysplorer. The results can be displayed in real time during the simulation of the sensitivity analysis.

产品简介 模型参数标定工具箱提供用于参数辨识的分析算法，如梯度下降、粒子群等算法，支持在既定参数范围对多个模型参数进行标定。利用模型参数标定工具箱可获得基于实验数据/目标数据的系统模型参数。 产品功能  数据导入 支持仿真或实验数据导入，显示导入数据，并与标定模型的目标变量关联。  参数标定 支持在既定参数范围对模型参数进行标定，提供梯度下降、粒子群等算法。  数据可视化 提供数据可视化插件，在模型标定过程中实时进行试验数据和仿真结果显示对比。

产品简介 模型设计优化工具箱提供不同的约束条件和适用于不同情况的单目标和多目标优化算法，支持模型多参数优化。利用模型设计优化工具箱可在合理范围和约束条件下进行模型参数调节和计算，获得最优参数，以满足复杂系统的性能设计指标。 产品功能  多种变量约束类型 模型设计优化工具箱对优化目标提供了多种估值函数和约束条件类型，如"FinalValue"估值函数按求解终止时刻的值计算目标函数，希望目标最小化等。  多实例优化 工具箱支持对目标模型创建多个边界实例，对多实例同时进行参数优化。  数据可视化 提供数据可视化插件，集成在MWORKS.Sysplorer中。可以在模型参数优化的仿真过程中实时显示迭代优化后参数的仿真结果。

About

Model Confidence Assessment Toolbox is a tool for quantitative evaluation of model confidence.

Model Confidence Assessment Toolbox is a tool for quantitative evaluation of model credibility, providing quantitative, qualitative and quantitative correlation combination of confidence assessment methods, outputting quantitative assessment results of simulation model/system credibility, which is an important support tool for VV&A of simulation system. The Model Confidence Assessment Toolbox can be used to quantitatively assess the credibility of simulation models/systems. Product Features

• Quantitative Assessment Support TIC, Average Euclidean distance coefficient, Correlation coefficient, Angular cosine coefficient, Steady state maximum erro and other confidence assessment algorithms, support different quantitative assessment methods. The confidence assessment algorithms such as
• Qualitative Assessment Provides qualitative assessment methods such as Hierarchical Analysis, Fuzzy Comprehensive Evaluation, Fuzzy Hierarchical Analysis, Gray Comprehensive Evaluation, and Fidelity-based Evaluation, realizing the combination of quantitative assessment and qualitative assessment.
• Generate assessment reports Provide report templates, support the automatic editing of diagrams and text of the model confidence assessment process and results into the report, and also support user-defined report templates.

Description

Co-Simulation Toolbox is a software for joint simulation of multidisciplinary heterogeneous models. Based on an open, object-oriented, graphical integration framework, it supports opening up the data interfaces between heterogeneous models, building joint simulation models, and realizing the reuse of the models developed by mainstream commercial software and commonly used programming languages; it supports the integration of local unified models and distributed integration; and it supports synchronization of the progress of the solution and control of each simulation unit. It supports the synchronization and control of the solution progress of each simulation unit, and carries out the joint simulation of multidisciplinary heterogeneous models. The joint simulation toolbox can realize cross-tool, cross-language and cross-domain joint simulation, which improves the simulation efficiency of complex systems.

Features

• Rich Heterogeneous Model/Tool Integration Interface Provide mainstream commercial software, external language interfaces such as C/C++/Fortran/Python, device interfaces such as mouse/keyboard/aircraft joystick, hardware communication interfaces such as Modbus, OPC, serial port, and view interfaces such as STK/FlightGear/Unity3D.
• FMI Interface It supports the international mainstream FMI standard, two modes of model exchange and joint simulation, two versions of FMI 1.0 and FMI 2.0.
• Visualized Heterogeneous Model Integration Provides a visualized heterogeneous model integration environment, supporting port configuration, port merging, port batch connection, configuration checking, and checking of component ID, simulation step, etc.
• One-stop simulation node management It supports unified allocation and management of simulation nodes on the server side, unified monitoring of simulation node status on the server side, and one-click start/stop of each simulation node on the server side.
• Distributed Collaborative Simulation It supports distributed parallel simulation and solving, and supports joint simulation of simulation nodes with the same step size or different step sizes; it supports two modes of independent simulation and real-time simulation, and supports the use of different solving algorithms by each simulation node.

Description

CAE model downgrading toolbox provides CAE model simulation data import and data cleaning, static downgrading model calculation, dynamic downgrading model calculation, export downgrading model and other functions. Using the CAE model downgrade toolbox, the 3D CAE model can be downgraded to a one-dimensional model at the system level, which ensures high accuracy while realizing a significant increase in the efficiency of the model simulation, and further realizes the joint simulation with the system model.

Features

• Data Cleaning and Data Import Supports importing simulation or experimental data, automatic data cleaning, and removal of bad data points; supports configuration of input and output variables, and division of training and validation sets.
• Reduced-order model training It supports setting the type of reduced-order model and solving parameters, and provides methods based on multi-layer feed-forward neural network, long and short-term memory neural network, response surface model, etc.; it supports pausing and re-training during the training of the model, and it supports real-time display of the residual convergence curve.
• Export to Modelica model It supports exporting the reduced-order model directly to Modelica model to realize the integration and joint simulation with the system model.
• 3D Visualization Provides 3D visualization components to support real-time display of 3D field results during the training process of the reduced-order model, and supports the use of cloud diagrams, grids, scatter plots, geometric trees and profiles for the display of simulation results.

About

Model Confidence Assessment Toolbox is a tool for quantitative evaluation of model confidence. FMI Import/Export Tool is a model standardization packaging and system integration tool, which supports packaging and exporting Modelica models to FMU models, importing FMU models and integrating them with Modelica models. With FMI Import/Export Toolbox, you can realize model encapsulation, system integration and co-simulation between MWORKS.Sysplorer environment and other software. Product Features

• Supports multiple FMI versions Supports FMU import and export based on FMI1.0 and FMI2.0 standards.
• Support multiple FMI modes Supports Model-Exchange and Co-Simulation modes.

Product Description

SysML-Modelica Conversion Toolbox provides tools for the conversion of SysML design models to Modelica simulation models, providing SysML model import, Modelica model generation, unified management of SysML models and Modelica models, real-time editing of Modelica models and other functions. Using SysML import toolbox, SysML design model can be automatically converted to Modelica simulation model to ensure the consistency of data transfer between design and simulation, and support the realization of the integration of design and simulation.

Product Functions

• SysML model standard support Support UML2.x XMI standard SysML design model import, generate Modelica simulation framework model.
• Unified management of design & simulation models Visualize and manage SysML design models and Modelica simulation framework, support model search and location.
• Simulation model auto-filling Support to establish the association between SysML design models and existing Modelica simulation models, and fill the simulation models into the simulation framework.
• Real-time Editing of Simulation Models Support real-time editing of generated Modelica simulation models.

Product Description

FEI is an application development toolkit consisting of the MWORKS kernel module and its service components, including a series of program interfaces, help files, development examples and utilities. The kernel module includes the Modelica compiler, analyzer, code generator, and solver; the service components include the Atomic Operations Interface and the Combinatorial Interface built on the kernel module. Sysplorer can be extended or specialized design and simulation applications can be developed using the extension interfaces.

Product Features

• Plug-in development Sysplorer plug-in development is supported to extend the functionality of MWORKS.Sysplorer, including but not limited to interface style, graphic elements, media handling, curve display and operation, batch parameterization, batch simulation, automation scripts, etc.
• Modeling and Simulation Capability Expansion It supports the expansion of modeling and simulation capabilities on the basis of MWORKS, integrating proprietary kernel modules to expand the modeling syntax semantics, integration algorithms, and solution algorithms according to the characteristics of various industries and professions, so as to meet the customized requirements.
• Industrial APP Development We support the development of industry APP and professional APP based on deep-level APIs for the actual needs of different fields, industries and major projects.