In determination of weights by learning, for linear input vectors what kind of learning should be employed?

Feedforward Neural Networks topics include: Pattern association, pattern classification, weight determination, pattern mapping and storage analysis and the technique of backpropagation algorithm. A feedforward neural network is a type of artificial neural network where the nodes' connections do not form a loop. Information flows in a forward manner only, from the input nodes, through the hidden nodes (if any), and to the output nodes.  Feedforward neural networks are also known as multi-layered networks of neurons (MLN). The simplest type of feedforward neural network is the perceptron,... Show more

Feedforward Neural Networks topics include: Pattern association, pattern classification, weight determination, pattern mapping and storage analysis and the technique of backpropagation algorithm.

A feedforward neural network is a type of artificial neural network where the nodes' connections do not form a loop. Information flows in a forward manner only, from the input nodes, through the hidden nodes (if any), and to the output nodes. 

Feedforward neural networks are also known as multi-layered networks of neurons (MLN). The simplest type of feedforward neural network is the perceptron, which has only an input layer and an output layer. 
Feedforward neural networks are suitable for tasks like pattern recognition and classification. They transmit data in one direction, from input to output, without feedback loops. 

Backpropagation is an algorithm used to train feedforward neural networks. 
It consists of two steps:
Forward propagation: Input is passed through the network, and the output is calculated.
Backward propagation: An error is calculated, and weights are updated to reduce error. 

Related: Neural Network Practice Test: Basics of Artificial Neural Networks

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