The Kak neural network 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. Simplified view of an artificial neural network A neural network is an interconnected group of biological neurons. ... The binary or base-two numeral system is a system for representing numbers in which a radix of two is used; that is, each digit in a binary numeral may have either of two different values. ...
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. Finance studies and addresses the ways in which individuals, businesses and organizations raise, allocate and use monetary resources over time, taking into account the risks entailed in their projects. ... Pattern recognition is a field within the area of machine learning. ... Signal processing is the processing, amplification and interpretation of signals and deals with the analysis and manipulation of signals. ...
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.
The Kakneuralnetwork is an instantaneously trained neuralnetwork 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.
This network has been successfully used in a variety of applications in finance, pattern recognition, signal processing, and time-series extrapolation.
Kak, New algorithms for training feedforward neuralnetworks.
A neuralnetwork is an interconnected groups of nodes, akin to the vast network of neurons in the human brain.
Neuralnetworks are particularly useful for dealing with bounded real-valued data, where a real-valued output is desired; in this way neuralnetworks will perform classification by degrees, and are capable of expressing values equivalent to "not sure".
The genome is then constitued of the networks parameters and the fitness of a network is the adequacy of the behaviour exhibited by the controlled robot (or often by a simulation of this behaviour).
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