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    NeuroIntelligence

    Category  Intelligent Software>Neural Network Systems/Tools

    Abstract  NeuroIntelligence is a neural network (NN) software
    application designed to assist experts in solving real-world problems.
    NeuroIntelligence features only proven algorithms and techniques; it is
    fast and easy-to-use. Product features/capabilities include:

    All stages supported -- NeuroIntelligence supports all stages of neural
    net design and application. It can be used to: 1) analyze and preprocess
    datasets; 2) find the best neural network architecture; 3) train, test and
    optimize neural network; 4) apply the network to new data.

    Advanced visualization – Offers visualized architecture search, network
    training, and testing. Architecture search: fitness bars, training graphs
    comparison. Training graphs: dataset error, network error, weights and
    errors distribution, input importance. Testing: actual vs. output graph,
    scatter plot, response graph, Receiver Operating Characteristic (ROC)
    curve, and confusion matrix.

    Improve your productivity -- The interface of NeuroIntelligence is
    optimized to solve forecasting, classification and function approximation
    problems. You can create a better solution much faster using the
    application's easy-to-use graphical user interface (GUI) and unique
    time-saving capabilities.

    Note: Many processes are automated but you can understand the
    underlying behavior with graphs, statistics and reports. Options and
    parameters are intelligible, comprehensive and accessible with one
    mouse click.

    Product key features/capabilities include:

    Data analysis and preprocessing -- Saves you time with automated
    column identification and encoding. Identify and encode date/time
    columns. Perform automatic and manual data partition. Identify and
    replace/remove data anomalies. Analyze your data with a response
    graph.

    Automated network selection -- Select fitness criteria along with
    architecture search method and NeuroIntelligence does the rest. It
    automatically finds the best architecture offering you graphs of the
    search process and details for every tested neural network.

    Proven training algorithms -- The most efficient algorithms, such as
    Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation,
    variations of Quasi-Newton and Back-Propagation, are available for
    neural network training.

    Proven techniques -- Improve network performance with early-stopping,
    generalization loss control, outliers and missing values handling,
    dataset partition, jogging weights, different activation and error
    functions, weights initialization and more.

    Ease of use -- NeuroIntelligence was developed with the highest
    attention to usability. It has an intelligible layout, options, reports and
    descriptions. It offers graphs and results visualization at all stages. You
    can save all graphs and tables, as well as other results with a single
    mouse click. Local toolbars and online help are always accessible.

    Application in several clicks -- After a neural network is tested it can be
    easily applied to new data. Results are visualized with a response
    graph. You can apply the selected network to a single case, data file or
    records from your input dataset. The whole project or only selected
    neural networks can be saved for future use.

    Additional product features/capabilities include:

    Analyze and Pre-process Your Data -- 1) Import Excel files; 2) Import
    popular American Standard Code for Information Interchange (ASCII)
    file formats [Comma Separated Values (CSV), TXT, PRN]; 3) Custom
    date formats and file structure definition; 4) Input dataset size is limited
    only by the hardware of the computer;  5) Categorical values encoding;
    6) Numeric values scaling; 7) Min/max values specification for numeric
    columns scaling; 8) Missing values handling for both numeric and
    categorical data; 9) Outliers handling for numeric data; 10) Automatic
    recognition of data entry errors (wrong type values); and more.

    Design Neural Network -- 1) Input feature selection [Genetic Algorithm
    (GA), stepwise, exhaustive]; 2) Manual architecture specification (up to 5
    hidden layers for multi-layer perceptron); 3) Heuristic architecture
    search with customizable range of search and sensitivity; 4) Exhaustive
    architecture search; 5) Customizable search range and search
    sensitivity; 6) Detailed statistics for each tested architecture; 7) Network
    fitness criteria: Akaike’s Information Criteria (AIC), Test set error,
    Correlation, R-squared;  8) Error functions: Sum-of-Squares, Cross-
    entropy; 9) Classification model: Winner-takes-all, Confidence-limits
    (Accept/Reject levels); 10) Automatic adjustment of learning rate and
    momentum for Back-Propagation algorithm; and more.

    Control Network Training Process -- 1) Real-time training error graph; 2)
    Real-time control on training parameters: a) errors on training and
    validation set: mean square error (MSE), mean absolute error (MAE),
    correct classification rate (CCR); b) error improvement; c) training
    speed (iterations per second); d) # of iterations; 3) Continue training
    with new parameters; 4) Jog weights; 5) Early-stopping on
    generalization loss; 6) Retain and restore best network; 7) Stopping
    conditions: a) target error on training and validation sets: MSE, MAE,
    CCR; b) error improvement: network error, dataset error; c) number of
    iterations; d) generalization loss; 8) Automatic network retrains and
    selection of the best network among retrains; 9) Retrains statistics; 10)
    Weights initialization: manual randomization range; optimized for
    Uniform or Gaussian distribution; and more.

    Apply Network -- 1) Enter new cases manually or insert from the
    Clipboard; 2) Load new cases from a new data file; 3) Apply to selected
    records from your original dataset; 4) Graphical network output
    representation; 5) Output representation with Results Table; 6)
    Confidence limits for network output; 7) Save results in a separate file or
    copy them to the Clipboard.

    General -- 1) Customizable interface; 2) Detailed reporting; 3) Online
    help system; 4) Free technical support; 5) Project files to keep all
    related information in one place; 6) Sample financial, marketing, real
    estate and scientific problems included.

    Note: See G6G Product Number 20111 for additional product info from
    this manufacturer.

    System Requirements  

    Pentium-compatible processor (Pentium II or higher recommended)
    Windows® 98, ME, 2000, XP or Vista
    Internet Explorer 5.0 or higher
    64 MB available RAM (128 MB recommended)
    256-color monitor capable of 800 x 600 screen resolution
    15 MB of free hard disk space

    Manufacturer   Home office.

    Alyuda Research
    864 Terrace Dr.
    Los Altos, CA 94024
    Phone: (510) 931 7808, (888) 862 2759, ext. 3
    Fax: (510) 279 5649     
    Product questions: sales@alyuda.com
    Technical support: support@alyuda.com
    Consulting services: sales@alyuda.com
    PR and marketing: marketing@alyuda.com.
    General questions and information requests: contacts@alyuda.com

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