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Robots are required to function in environments that are not known when the robot is programmed. The solution is to have the robot learn algorithms by itself. Artificial neural networks (ANN) are computerized models of neurons and their connections that over time can adapt themselves to perform a task. An ANN is defined by its topology: the number of neurons, the number of levels between the inputs and outputs, and the connections between neurons of adjacent levels. The second component of an ANN is an algorithm for learning. The Hebbian rule is an elementary form of reinforcement learning where the ANN receives feedback on which behaviors are good and which are not. The feedback is used to adjust the weights given to the input of each neuron in the ANN.
Haykin, S.O.: Neural Networks and Learning Machines, 3rd edn. Pearson, Boston (2008)
Herculano-Houzel, S.: The human brain in numbers: a linearly scaled-up primate brain. Front. Hum. Neurosci. 3, 31 (2009) CrossRef
Kriesel, D.: A Brief Introduction to Neural Networks. http://www.dkriesel.com/en/science/neural_networks (2007)
Rojas, R.: Neural Networks: A Systematic Introduction. Springer, Berlin (1996) CrossRef
- Neural Networks
- Chapter 13