Han et al., 2018 - Google Patents
Interval type-2 fuzzy neural networks for chaotic time series prediction: A concise overviewHan et al., 2018
- Document ID
- 9685542787729142321
- Author
- Han M
- Zhong K
- Qiu T
- Han B
- Publication year
- Publication venue
- IEEE transactions on cybernetics
External Links
Snippet
Chaotic time series widely exists in nature and society (eg, meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have …
- 230000000739 chaotic 0 title abstract description 83
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
- G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
- G06N5/043—Distributed expert systems, blackboards
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Han et al. | Interval type-2 fuzzy neural networks for chaotic time series prediction: A concise overview | |
| Zhang et al. | Efficient federated learning for cloud-based AIoT applications | |
| Wang et al. | ChatGPT for computational social systems: From conversational applications to human-oriented operating systems | |
| Chu et al. | Weighted broad learning system and its application in nonlinear industrial process modeling | |
| Yang et al. | SNIB: improving spike-based machine learning using nonlinear information bottleneck | |
| Tang et al. | A novel wind speed interval prediction based on error prediction method | |
| Qin et al. | A dual-stage attention-based recurrent neural network for time series prediction | |
| Sun et al. | A review of designs and applications of echo state networks | |
| Heravi et al. | A new correntropy-based conjugate gradient backpropagation algorithm for improving training in neural networks | |
| Bao et al. | Correntropy-based evolving fuzzy neural system | |
| Katal et al. | Artificial neural network: models, applications, and challenges | |
| Behbood et al. | Multistep fuzzy bridged refinement domain adaptation algorithm and its application to bank failure prediction | |
| Cao et al. | Online sequential extreme learning machine with dynamic forgetting factor | |
| Al Bataineh et al. | Immunocomputing-based approach for optimizing the topologies of LSTM networks | |
| Acevedo-Mosqueda et al. | Bidirectional associative memories: Different approaches | |
| CN108898214A (en) | A kind of online sequence data prediction technique and device | |
| Li et al. | Online and self-learning approach to the identification of fuzzy neural networks | |
| Jovanovic et al. | Gold prices forecasting using recurrent neural network with attention tuned by metaheuristics | |
| Soto et al. | Particle swarm optimization of the fuzzy integrators for time series prediction using ensemble of IT2FNN architectures | |
| Bai et al. | Cooperative multi-agent reinforcement learning with hypergraph convolution | |
| Jiang | An attention GRU-XGBoost model for stock market prediction strategies | |
| Ben-Bright et al. | Taxonomy and a theoretical model for feedforward neural networks | |
| Li et al. | Random fuzzy clustering granular hyperplane classifier | |
| Anuar et al. | Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach | |
| Li et al. | White learning: A white-box data fusion machine learning framework for extreme and fast automated cancer diagnosis |