MathWorks Announces Release 2018a of the MATLAB and Simulink Product Families

NATICK, Mass. — (BUSINESS WIRE) — March 15, 2018MathWorks today introduced Release 2018a (R2018a) with a range of new capabilities in  MATLAB and  Simulink. R2018a includes two new products, Predictive Maintenance Toolbox for designing and testing condition monitoring and predictive maintenance algorithms, and Vehicle Dynamics Blockset for modeling and simulating vehicle dynamics in a virtual 3D environment. In addition to new features in MATLAB and Simulink, and the new products, this release also includes updates and bug fixes to 94 other products.

MATLAB Product Family Updates Include:

  • MATLAB:
    • Live functions, documentation authoring, debugging, and interactive controls for embedding sliders and drop-down menus in the Live Editor
    • App (UI) testing framework, C++ MEX interface, custom tab completion, and function assistants for advanced software development
  • MATLAB Online:
    • Hardware connectivity for communicating with USB webcams
  • Econometrics Toolbox:
    • Econometric Modeler app for performing time series analysis, specification testing, modeling, and diagnostics
  • Image Processing Toolbox:
    • 3-D image processing and volume visualization
  • Partial Differential Equation Toolbox:
    • Structural dynamic analysis to find natural frequencies, mode shapes, and transient response
  • Optimization Toolbox:
    • Branching methods for solving mixed-integer linear problems faster

Deep Learning

  • Neural Network Toolbox:
    • Support package for importing deep learning layers and networks designed in TensorFlow-Keras
    • Long short-term memory (LSTM) networks for solving regression problems, and doing text classification with Text Analytics Toolbox
    • Adam, RMSProp, and gradient clipping to improve network training
    • Accelerated training for directed acyclic graph (DAG) networks using multiple GPUs and computing intermediate layer activations
  • Computer Vision System Toolbox:
    • Image Labeler app to automate labeling of individual pixels for semantic segmentation
  • GPU Coder:
    • CUDA code generation for networks with directed acyclic graph (DAG) topology and pretrained networks like GoogLeNet, ResNet, and SegNet
    • C code generation for deep learning networks on Intel and ARM processors

Data Analytics

  • Statistics and Machine Learning Toolbox:
    • High-density data visualization with scatter plots in the Classification Learner app

    • Big data algorithms for kernel SVM regression, computing confusion matrices, and creating nonstratified partitions for cross-validation
  • Text Analytics Toolbox:
    • Multiword phrase extraction and counting, HTML text extraction, and detection of sentences, email addresses, and URLs
    • Stochastic LDA model training for large datasets
  • Predictive Maintenance Toolbox:
    • A new product for designing and testing condition monitoring and predictive maintenance algorithms

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