ADALINE Neural Network
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An ADALINE Neural Network is an Single-Layer Neural Network that is based on McCulloch-Pitts Neuron developed by Widrow (1960).
- AKA: ADALINE; Adaptive Linear Neuron; Adaptive Linear Element.
- Context:
- It can be trained by a ADALINE Training System.
- …
- Example(s):
- Counter-Example(s):
- See: Artificial Neural Network, Transfer Function, Memistor, Bernard Widrow, Marcian Hoff, Stanford University, McCulloch–Pitts Neuron, Perceptron.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/ADALINE Retrieved:2019-4-14.
- ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. [1] [2] [3] [4] The network uses memistors. It was developed by Professor Bernard Widrow and his graduate student Ted Hoff at Stanford University in 1960. It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function.
The difference between Adaline and the standard (McCulloch–Pitts) perceptron is that in the learning phase, the weights are adjusted according to the weighted sum of the inputs (the net). In the standard perceptron, the net is passed to the activation (transfer) function and the function's output is used for adjusting the weights.
A multilayer network of ADALINE units is known as a MADALINE.
- ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. [1] [2] [3] [4] The network uses memistors. It was developed by Professor Bernard Widrow and his graduate student Ted Hoff at Stanford University in 1960. It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function.
1960
- (Widrow, 1960) ⇒ Bernard Widrow. (1960). “Adaptive "Adaline" Neuron Using Chemical "memistors".” Number Technical Report 1553-2. Stanford Electron. Labs. Stanford, CA.