Neural
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Neural Creator v.1.0
Neural Creator is our visually based neural network development system that can be trained to perform pattern matching, feature recognition or make predictions on noisy or fuzzy data.
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Neural Pattern Recognition v.6.0
Neural Pattern Recognition is implemented using Kohonen neural network and can recognize common stock patterns such as Head & Shoulders, Double Tops, Double Bottoms, Rounding Top, Rounding Bottom, Up Trend, Down Trend, Range, Triangle.
Olsoft Neural Network Library v.1.0
OLSOFT Neural Network Library is the class to create, learn and use Back Propagation neural networks and SOFM (Self-Organizing Feature Map). The library makes integration of neural networks' functionality into your own applications easy and seamless.
NeuralCode Neural Networks Trading v.1.0
NeuralCode is an industrial grade Artificial Neural Networks implementation for financial prediction. The software can take data like the Opening price, High, Low,
Neural Network Component (ActiveX) v.1.0
Artificial Neural System Component is designed for researchers in the fields of machine learning, it can be used to construct Back Propagation Neural Network and to train it with provided samples, then finally recall it with appropriate data. This
Artificial Neural Network v.1.0
Create artificial neural networks. Artificial Neural Network demonstrate artificial intelligence. Taking advantage of serialization, there are two parts of the network. The actual network, and then training data.
Neural Networks v.4.3.7
Create neural networks fast and easy. Inspired by neurons and their connections in the brain, neural network is a representation used in machine learning. After running the back-propagation learning algorithm on a given set of examples,
Interactive Neural Network Simulator v.1.0
iSNS is an interactive neural network simulator written in Java/Java3D.
Java Neural Modeling Framework new GUI v.1.0
Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.