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Lernmatrix
Lernmatrix, an associative-memory-like architecture of an artificial neural network, invented around 1960 by Karl Steinbuch Karl W. Steinbuch (June 15, 1917 in Stuttgart-Bad Cannstatt – June 4, 2005 in Ettlingen) was a German computer scientist, cyberneticist, and electrical engineer. He was an early and influential researcher of German computer science, and was .... External links A new theoretical framework for the Steinbuch's Lernmatrix [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Karl Steinbuch
Karl W. Steinbuch (June 15, 1917 in Stuttgart-Bad Cannstatt – June 4, 2005 in Ettlingen) was a German computer scientist, cyberneticist, and electrical engineer. He was an early and influential researcher of German computer science, and was the developer of the Lernmatrix, an early implementation of artificial neural networks. Steinbuch also wrote about the societal implications of modern media. Biography Steinbuch studied at the University of Stuttgart and in 1944 he received his PhD in physics. In 1948 he joined Standard Elektrik Lorenz (SEL, part of the ITT group) in Stuttgart, as a computer design engineer and later as a director of research and development, where he filed more than 70 patents. There Steinbuch completed the first European fully transistorized computer, the ER 56 marketed by SEL. In 1958 he became professor and director of the institute of technology for information processingITIV of the University of Karlsruhe, where he retired in 1980. In 1967 he ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
