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Stuttgart Neural Network Simulator
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Stuttgart Neural Network Simulator

Overview
Synopsis

The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R.

Category

Artificial Neural Network Software

Features

•Encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks
•Accessibility of all of the SNNS algorithmic functionality from R using a low-level interface
•A high-level interface for convenient, R-style usage of many standard neural network procedures

License

Proprietary Software

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Company

Stuttgart Neural Network Simulator

What is best?

•Encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks
•Accessibility of all of the SNNS algorithmic functionality from R using a low-level interface

PAT Rating™
Editor Rating
Aggregated User Rating
Rate Here
Ease of use
8.8
6.2
Features & Functionality
8.8
8.0
Advanced Features
8.7
6.9
Integration
8.6
9.3
Performance
8.6
Customer Support
7.4
Implementation
Renew & Recommend
Bottom Line

Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.

8.7
Editor Rating
7.6
Aggregated User Rating
2 ratings
You have rated this

The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed.

Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R. The package provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS.

It includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNS file formats. Following its initial release, its developers sought to make various improvements including fixing the remaining memory leaks, detected by valgrind, using the "colorspace" package for heatmaps, adding jss paper as vignette and making it possible to pass already trained models in the high-level functions, and use them to initialize the network.

The SNNS is a comprehensive application for neural network model building, training, and testing. It is one of the most complete, most reliable, and fastest implementations of neural network standard procedures. RSNNS is a general purpose comprehensive neural network package for R because of the functionality and flexibility of the SNNS kernel that is provided within R, its convenient interfaces to the most common tasks, so that the methods of SNNS integrate seamlessly into R and its enhanced tools for visualization and analysis of training and testing of the networks.

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Ease of use
Features & Functionality
Advanced Features
Integration
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Customer Support
Implementation
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