Turing’s Connectionism: An Investigation of Neural Network ArchitecturesSpringer Science & Business Media, 2012. gada 6. dec. - 200 lappuses Alan Mathison Turing (1912-1954) was the first to carry out substantial re search in the field now known as Artificial Intelligence (AI). He was thinking about machine intelligence at least as early as 1941 and during the war cir culated a typewritten paper on machine intelligence among his colleagues at the Government Code and Cypher School (GC & CS), Bletchley Park. Now lost, this was undoubtedly the earliest paper in the field of AI. It probably concerned machine learning and heuristic problem-solving; both were topics that Turing discussed extensively during the war years at GC & CS, as was mechanical chess [121]. In 1945, the war in Europe over, Turing was recruited by the National Physical Laboratory (NPL)! in London, his brief to design and develop an electronic stored-program digital computer-a concrete form of the universal Turing machine of 1936 [185]. Turing's technical report "Proposed Electronic 2 Calculator" , dating from the end of 1945 and containing his design for the Automatic Computing Engine (ACE), was the first relatively complete spec ification of an electronic stored-program digital computer [193,197]. (The document "First Draft of a Report on the EDVAC", produced by John von Neumann and the Moore School group at the University of Pennsylvania in May 1945, contained little engineering detail, in particular concerning elec tronic hardware [202]. |
No grāmatas satura
1.–5. rezultāts no 31.
xv. lappuse
... complex - systems - theory point of view made a bridge between his connectionist ideas and his work on morphogenesis which formed the basis for the modern non - linear dynamical systems theory . Undoubtedly , most of the modern ...
... complex - systems - theory point of view made a bridge between his connectionist ideas and his work on morphogenesis which formed the basis for the modern non - linear dynamical systems theory . Undoubtedly , most of the modern ...
xx. lappuse
... Complex Systems 74 3.8 Hardware Implementation 77 4. Organizing Unorganized Machines 83 4.2.1 4.1 Evolutionary Algorithms 4.2 Evolutionary Artificial Neural Networks Fundamentals ... 4.2.2 Encoding Techniques 84 86 86 87 4.2.3 A - type ...
... Complex Systems 74 3.8 Hardware Implementation 77 4. Organizing Unorganized Machines 83 4.2.1 4.1 Evolutionary Algorithms 4.2 Evolutionary Artificial Neural Networks Fundamentals ... 4.2.2 Encoding Techniques 84 86 86 87 4.2.3 A - type ...
3. lappuse
... complex than Turing's neurons . They can have as many inputs as they like and provide inhibitory and exhibitory synapses . Like Turing's model , they make , however , no use of weighted connections . Turing's networks and McCulloch and ...
... complex than Turing's neurons . They can have as many inputs as they like and provide inhibitory and exhibitory synapses . Like Turing's model , they make , however , no use of weighted connections . Turing's networks and McCulloch and ...
4. lappuse
... complex and beautiful ( e.g. , symmetry in nature , etc. ) , emerge from the viewpoint of nonlinear physics and chemistry . Already in his child- hood he had been spotted and sketched " watching the daisies grow " [ 91 ] . The interest ...
... complex and beautiful ( e.g. , symmetry in nature , etc. ) , emerge from the viewpoint of nonlinear physics and chemistry . Already in his child- hood he had been spotted and sketched " watching the daisies grow " [ 91 ] . The interest ...
6. lappuse
Esat sasniedzis šīs grāmatas aplūkošanas reižu limitu.
Esat sasniedzis šīs grāmatas aplūkošanas reižu limitu.
Saturs
1 | |
Intelligent Machinery | 17 |
Synthesis of Logical Functions and Digital Systems with | 63 |
Organizing Unorganized Machines | 83 |
Network Properties and Characteristics | 120 |
Epilogue | 169 |
List of Tables 181 | 180 |
Index | 197 |
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Turing’s Connectionism: An Investigation of Neural Network Architectures Christof Teuscher Ierobežota priekšskatīšana - 2002 |
Bieži izmantoti vārdi un frāzes
100-node A-type network A-type machine A-type network A-type unorganized machine artificial neural networks attractor length attractors B-type link behaviour binary bitstream boolean networks brain Church-Turing thesis clock cycles complex configuration connectionism connectionist Copeland and Proudfoot CP-type Definition delay described disabled dynamical systems enabled connections encoding evolution evolutionary algorithm example finite flip-flop four-input function realized genetic algorithm hardware Hypercomputation implementation incoming links initial node values input nodes inputs and outputs interconnection switches inverter L-system links per node logical functions MATLAB McCulloch-Pitts monostable multiplexer NAND gate network architecture network genome network nodes network output neurons Output activity output nodes parameters pattern classification possible presented problem Proposition random boolean networks randomly Section self-organizing shift-register shown in Figure shows signal simulated stable std_logic supervised learning synchronous tape TBI-type network threshold elements Turing networks Turing neural networks Turing's neural networks Two-input Typical activities unit universal Turing machine vector VHDL weights