right|thumb|241x241px|Learning inside a single-layer ADALINE thumb|286x286px|Photo of an ADALINE machine, with hand-adjustable weights implemented by rheostats thumb|346x346px|Schematic of a single ADALINE unit 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 it. It was developed by professor Bernard Widrow and his doctoral student Marcian Hoff at Stanford University in 1960. It is based on the perceptron and consists of weights, a bias, and a summation function. The weights
right|thumb|241x241px|Learning inside a single-layer ADALINE thumb|286x286px|Photo of an ADALINE machine, with hand-adjustable weights implemented by rheostats thumb|346x346px|Schematic of a single ADALINE unit 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 it. It was developed by professor Bernard Widrow and his doctoral student Marcian Hoff at Stanford University in 1960. It is based on the perceptron and consists of weights, a bias, and a summation function. The weights and biases were implemented by rheostats (as seen in the "knobby ADALINE"), and later, memistors. It found extensive use in adaptive signal processing, especially of adaptive noise filtering.
The difference between Adaline and the standard (Rosenblatt) perceptron is in how they learn. Adaline unit weights are adjusted to match a teacher signal, before applying the Heaviside function (see figure), but the standard perceptron unit weights are adjusted to match the correct output, after applying the Heaviside function.
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).