Kak neural network
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Revision as of 20:29, 2 September 2021 by imported>Steve Marsio (over-write)
The Kak neural network, which was first proposed by Subhash Kak, is an instantaneously trained neural network that creates a new hidden neuron for each training sample, achieving instantaneous training for binary data and also for real data if some small additional processing is allowed. These networks, therefore, model short-term biological memory. The training algorithm for binary data creates links to the new hidden node that simply reflects the 0 and 1 values in the training vector. Hence there is no computation involved. This network has been successfully used in a variety of applications in finance, pattern recognition, signal processing, and time-series extrapolation.
References
- S. Kak, New algorithms for training feedforward neural networks. Pattern Recognition Letters 15, 1994, pp.295-298.
- S. Kak, On generalization by neural networks. Information Sciences 111, 1998, pp. 293-302.
External links
- FPGA implementation of Kak neural network
- Optical implementation of Kak network
- The Basic Kak network with complex inputs
- Implementing Kak Neural Networks on a Reconfigurable Computing Platform