Neural Designer is a desktop application for data mining which uses neural networks, a main paradigm of machine learning.
Artificial Neural Network Software, Data Mining Software
High performance computing
Easy to use
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
Neural Designer is a desktop application for data mining which uses neural networks, a main paradigm of machine learning. The software is developed by the startup company called Artelnics, based in Spain and founded by Roberto Lopez and Ismael Santana. Neural networks are mathematical models of the brain function, computational models which are inspired by central nervous systems, in particular the brain, which can be trained to perform certain tasks. Neural networks are capable of machine learning as well as pattern recognition. Neural networks are generally presented as systems of interconnected neurons, which can compute outputs from inputs. Neural network models can be used to infer a function from observations. This is particularly useful in applications where the complexity of the data or task makes the design impractical. Some of the examples where neural networks can be used are regression analysis, such as time series prediction, fitness approximation and modeling, and in classification, such as pattern and sequence recognition, novelty detection and sequential decision making. It can also be used in data processing, including filtering, clustering, blind source separation and compression and in robotics and control. Neural network software is used in simulation, research and for analysis in a wider array of adaptive systems.
Neural Designer for data mining using Neural Networks
Neural Designer is a professional application for discovering complex relationships, recognizing unknown patterns and predicting actual trends from data sets by means of neural networks. Some of the examples where Neural Designer has used are in flight data to increase comfort and reduce consumption of aircrafts, in medical databases to make more reliable and less invasive diagnosis. Neural Designer has also used in physico-chemical data to increase the quality of wines and in sales data to optimize provisioning and to improve work quadrants.
Features of Neural Designer
The input to Neural Designer is a data set, and the output from it is a neural model. That neural model takes the form of a mathematical expression which can be exported to any computer language or system. The next figure shows the activity diagram of Neural Designer.
Neural Designer contains the machine learning algorithm, neural networks. Some of the features include complete instances and variables setting utilities, several data pre-analysis tools, unlimited network architecture. scaling, unscaling and probabilistic layers, different model outputs and derivatives calculations, neural network equation exporting. many objective and regularization functionals, many different training algorithms, extensive testing analysis methods. And comprehensive results as a report with many graphics
Components of Neural Designer
Neural Designer has three main components:
1. Neural Editor
2. Neural Engine
3. Neural Viewer
The editor lets you see and manipulate your settings. The input to Neural Editor is a data file, which can be of any type. The following figure shows the start page of Neural Editor.
Opening, for instance, the breast cancer diagnosis example, the whole application is shown.
The engine runs tasks. That component does not have any window, but it executes processes in background. Tasks are called from Neural Editor. The results will show up in Neural Viewer.
Neural Engine has been built using the open neural networks library OpenNN. OpenNN is an open source class library which implements neural networks and is written in C++.
The viewer displays the texts, tables, and graphs which result from running tasks. It writes a report that can be exported to different formats. The following figure illustrates how Neural Viewer shows the results from tasks.
Neural Designer is an advanced predictive analytics software using neural networks, in terms of design, usability, performance and technical support.