Hybrid Kohonen self-organizing map
In artificial neural networks, a hybrid Kohonen self-organizing map is a type of self-organizing map (SOM) named for the Finnish professor Teuvo Kohonen, where the network architecture consists of an input layer fully connected to a 2–D SOM or Kohonen layer.
The output from the Kohonen layer, which is the winning neuron, feeds into a hidden layer and finally into an output layer. In other words, the Kohonen SOM is the front–end, while the hidden and output layer of a multilayer perceptron is the back–end of the hybrid Kohonen SOM. The hybrid Kohonen SOM was first applied to machine vision systems for image classification and recognition.[1]
Hybrid Kohonen SOM has been used in weather prediction and especially in forecasting stock prices, which has made a challenging task considerably easier. It is fast and efficient with less classification error, hence is a better predictor, when compared to Kohonen SOM and backpropagation networks.[2]
References
- ↑ F. Nabhani and T. Shaw. Performance analysis and optimisation of shape recognition and classification using ANN. Robotics and Computer Integrated Manufacturing, 18:177–185, 2002.
- ↑ Mark O. Afolabi and Olatoyosi Olude (2007), Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM), in 40th Annual Hawaii International Conference On System Sciences’, 2007, IEEE, pp. 48–56.
