Neural Network Software Comparison

Products & Solutions / Products Comparison

The three Alyuda neural networks-based tools are targeted toward users who have different goals. All of them are designed to solve real-world problems. All share similar GUI oncepts and Alyuda proprietary heuristics. To find out which of the products suits you best, use the feature comparison table below.
Feature	Alyuda Forecaster XL	Alyuda Forecaster	Alyuda NeuroIntelligence
General
Excel add-in interface (optimized for MS Excel users)
Wizard-like interface (different modes for beginners and experts)
Windows tabbed interface (optimized for experts)
Automatic and manual data analysis and preprocessing
Automatic selection of neural network architecture and training parameters
Online help system
Free technical support
Sample financial, business and scientific problems included
Analyze and Pre-process Your Data
Import popular ASCII file formats (CSV, TXT, PRN)
Import Excel files)
Custom date formats and file structure definition
Automatic data analysis and pre-processing
Automatic categorical values encoding
Automatic numeric values scaling
Automatic Date/Time values encoding
Manual min/max values specification for scaling
Visual representation of data anomalies
Outliers handling for numeric data (customizable outlier coefficient)
Missing values handling for numeric values (removal and 4 substitution options)
Missing values handling for categorical values (removal and 3 substitution options)
Automatic recognition of data entry errors (wrong type values)
Detailed data analysis and data preprocessing reports
Automatic dataset partition to training, validation and test sets (random or sequential)
Manual dataset partition to training, validation and test sets
Manual column type identification (numeric, categorical, date, time, text)
Accept/ignore records and columns manually
Preprocessed data representation
Binary columns for anomalies indication
Two methods of automatic lag columns insertion
Statistical information for data columns
Design Neural Network
Input feature selection (GA, stepwise, exhaustive).
Fully automated neural network design with a constructive algorithm.
Fully automated neural network design using architecture search heuristics
Manual architecture specification (for multi-layer perceptron)
Customizable heuristic architecture search method
Three heuristic methods of neural network architecture search.
Exhaustive architecture search with customizable parameters
Customizable search range and search sensitivity
Detailed statistics for each tested architecture
Network fitness criteria: AIC, Test set error, Correlation, R-squared
Graphical representation of network fitness
Time-series networks
Network visualization
Network sets
Automatic adjustment of learning rate and momentum for Back-Propagation algorithm
Training algorithms: Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, Incremental and Batch Back-Propagation
Additional training algorithms: Quasi-Newton, Quasi-Newton (Limited Memory)
Activation functions: Linear, Logistic, Tanh, Softmax
Error functions: Sum-of-Squares, Cross-entropy
Classification model: Winner-takes-all, Confidence-limits (Accept/Reject levels)
Heuristics for automatic generation of stop training conditions
Generalization loss control (10 preset levels)
Retrain network to get better results
Manual stopping conditions (target error level, error improvement, correct classification rate, number of iterations)
Real-time control on training parameters (MSE, MAE, CCR, # of iterations).
Training Error Graph (network error by iteration)
Training Error Table (network error and error improvement by iteration)
Control Network Training Process
Real-time output of training parameters
Continue training with new parameters
Jog weights
Add jitter
Correlation and r-squared real-time graphs
Error improvement graph
Weights distribution graph
Error distribution graph
Input importance graph
Training log: test and validation set error for each iteration
Early-stopping on generalization loss
Retain and restore best network
Automatic network retrains and selection of the best network among retrains
Manual network retrain
Retrains statistics
Weights initialization: manual randomization range; optimized for Uniform or Gaussian distribution
Test and Analyze Performance
Actual vs Forecasted graph
Actual vs Forecasted scatter plot
Confusion matrix
Response graph
ROC curve
Actual vs Forecasted table with absolute and relative errors
Tolerance levels to quickly estimate overall forecasting quality
Input importance graph
Estimated forecasting error
Apply Network
Enter new cases manually or from the Clipboard
Load new cases from a new data file
Apply to selected records from your original dataset
Visual output representation with Response Graph
Output representation with Results Table
Confidence limits for network output
Save results in a separate file
Enjoy User Interface Extras
Detailed explanations on every step
Customizable reports (with preview and printing capabilities)
Reports export to HTML and XLS
Save/Load neural network
Two convenient methods of data selection in one interface (by range and by column)
Complete color customization for reports and graphs
Neural network autosave
Price
Single User Edition	$ 197	$ 187	$ 497
Unlimited Site Edition	$ 997	$ 997	$ 4970

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大奖官方网

t投彩彩票

ag真人平台

中国福彩app下载安装

同乐成平台

助赢彩票计划网页版本

彩乐乐网站

彩票网站大全64940_com官网

万博|体育手机版登陆

酷游|体育|平台