A comparison of algorithms for neuron-like cells
AIP Conference Proceedings 151 on Neural Networks for Computing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural networks for control
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Organizing Maps
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory
Computers and Electrical Engineering
Fuel cell emulator for oxygen excess ratio estimation on power electronics applications
Computers and Electrical Engineering
Computers and Electrical Engineering
A comparative study of wavelet families for classification of wrist motions
Computers and Electrical Engineering
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Artificial neural networks are some kind of data processing systems, which try to simulate features of the human brain and its learning process. So, they are widely used by researchers to solve different problems in optimization, classification, pattern recognition, associative memory and control. In this paper, an educational tool, which can be used to work on different kinds of neural network models and learn fundamentals of the artificial neural network, is described. At this point, the whole tool environment provides an advanced system to ensure mentioned functions. The developed system supports using MLP, LVQ and SOM models and related learning algorithms. It employs some visual, interactive tools, which enable users to compose their own neural networks and work on the developed networks easily. By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.