Indent Level: | ||
| Version: 2017.08 | ||
CPC | COOPERATIVE PATENT CLASSIFICATION | ||
G06N | COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS [2013-01] |
G06N 3/00 | Computer systems based on biological models (analogue computers simulating functional aspects of living beings G06G 7/60) [2013-01] |
G06N 3/002 | . | {Biomolecular computers, i.e. using biomolecules, proteins, cells (using DNA G06N 3/123; using neurons G06N 3/061)} [2013-01] |
G06N 3/004 | . | {Artificial life, i.e. computers simulating life} [2013-01] |
G06N 3/006 | . . | {based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds (computer games A63F 13/00; medical simulations G06F 19/00; information retrieval G06F 17/30873; image processing G06T; telecommunication protocols H04L 29/06034)} [2013-01] |
G06N 3/008 | . . | {based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior (toys or dolls A63H 3/00; industrial robot control G05B 19/00, B25J 9/00; artificial neural networks G06N 3/00; rule based artificial intelligence G06N 5/00)} [2013-01] |
G06N 3/02 | . | using neural network models (for adaptive control G05B 13/00; for image pattern matching G06K 9/00; for image data processing G06T 1/20; for phonetic pattern matching G10L 15/16) [2013-01] |
G06N 3/04 | . . | Architectures, e.g. interconnection topology [2013-01] |
G06N 3/0409 | . . . | {Adaptive Resonance Theory [ART] networks} [2013-01] |
G06N 3/0418 | . . . | {using chaos or fractal principles} [2013-01] |
G06N 3/0427 | . . . | {in combination with an expert system} [2013-01] |
G06N 3/0436 | . . . | {in combination with fuzzy logic} [2013-01] |
G06N 3/0445 | . . . | {Feedback networks, e.g. hopfield nets, associative networks} [2013-01] |
G06N 3/0454 | . . . | {using a combination of multiple neural nets} [2013-01] |
G06N 3/0463 | . . . | {Neocognitrons} [2013-01] |
G06N 3/0472 | . . . | {using probabilistic elements, e.g. p-rams, stochastic processors} [2013-01] |
G06N 3/0481 | . . . | {Non-linear activation functions, e.g. sigmoids, thresholds} [2013-01] |
G06N 3/049 | . . . | {Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs} [2013-01] |
G06N 3/06 | . . | Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons [2013-01] |
G06N 3/061 | . . . | {using biological neurons, e.g. biological neurons connected to an integrated circuit} [2013-01] |
G06N 3/063 | . . . | using electronic means [2013-01] |
G06N 3/067 | . . . | using optical means [2013-01] |
G06N 3/08 | . . | Learning methods [2013-01] |
G06N 3/082 | . . . | {modifying the architecture, e.g. adding or deleting nodes or connections, pruning} [2013-01] |
G06N 3/084 | . . . | {Back-propagation} [2013-01] |
G06N 3/086 | . . . | {using evolutionary programming, e.g. genetic algorithms} [2013-01] |
G06N 3/088 | . . . | {Non-supervised learning, e.g. competitive learning} [2013-01] |
G06N 3/10 | . . | Simulation on general purpose computers [2013-01] |
G06N 3/12 | . | using genetic models [2013-01] |
G06N 5/00 | Computer systems utilising knowledge based models [2013-01] |
G06N 5/003 | . | {Dynamic search techniques, heuristics, branch-and-bound (G06N 5/046 take precedence; for optimisation G06Q 10/04)} [2013-01] |
G06N 5/02 | . |
G06N 5/022 | . . | {Knowledge engineering, knowledge acquisition} [2013-01] |
G06N 5/025 | . . . | {Extracting rules from data (learning in general G06F 15/18)} [2013-01] |
G06N 5/027 | . . | {Frames} [2013-01] |
G06N 5/04 | . | Inference methods or devices [2013-01] |
G06N 5/041 | . . | {Abduction} [2013-01] |
G06N 5/042 | . . | {Backward inferencing} [2013-01] |
G06N 5/043 | . . | {Distributed expert systems, blackboards} [2013-01] |
G06N 5/045 | . . | {Explanation of inference steps} [2013-01] |
G06N 5/046 | . . | {Forward inferencing, production systems} [2013-01] |
G06N 5/048 | . . | {Fuzzy inferencing} [2013-01] |
G06N 7/00 | Computer systems based on specific mathematical models [2013-01] |
G06N 7/005 | . | {Probabilistic networks} [2013-01] |
G06N 7/02 | . |
G06N 7/023 | . . | {Learning or tuning the parameters of a fuzzy system} [2013-01] |
G06N 7/026 | . . | {Development tools for entering the parameters of a fuzzy system} [2013-01] |
G06N 7/04 | . . | Physical realisation [2013-01] |
G06N 7/043 | . . . | {Analogue or partially analogue implementation} [2013-01] |
G06N 7/046 | . . . | {Implementation by means of a neural network (neural networks using fuzzy logic G06N 3/0436)} [2013-01] |
G06N 7/06 | . . | Simulation on general purpose computers [2013-01] |
G06N 7/08 | . | using chaos models or non-linear system models [2013-01] |
G06N 99/00 | Subject matter not provided for in other groups of this subclass [2013-01] |
G06N 99/002 | . | {Quantum computers, i.e. information processing by using quantum superposition, coherence, decoherence, entanglement, nonlocality, teleportation} [2013-01] |
G06N 99/005 | . | {Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run (neural networks G06N 3/02; knowledge based models G06N 5/00; fuzzy logic systems G06N 7/02; adaptive control systems G05B 13/00)} [2013-01] |
G06N 99/007 | . | {Molecular computers, i.e. using inorganic molecules (using biomolecules G06N 3/002)} [2013-01] |