Zebhi et al., 2024 - Google Patents
Macromodeling of Nonlinear High-Speed Circuits Using Novel Hybrid Bidirectional High-Order Deep Recurrent Neural NetworkZebhi et al., 2024
- Document ID
- 774577007787492796
- Author
- Zebhi S
- Sadrossadat S
- Na W
- Zhang Q
- Publication year
- Publication venue
- IEEE Transactions on Circuits and Systems I: Regular Papers
External Links
Snippet
A new structure and macromodeling approach which is an advance over high-order recurrent neural network named bidirectional high-order deep recurrent neural network (BIHODRNN) is proposed in this paper for the first time for nonlinear circuits. In the proposed …
- 230000000306 recurrent effect 0 title abstract description 50
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
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
- G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5036—Computer-aided design using simulation for analog modelling, e.g. for circuits, spice programme, direct methods, relaxation methods
-
- 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
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- 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
- G06F17/20—Handling natural language data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
- G06F2217/78—Power analysis and optimization
-
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Budak et al. | An efficient analog circuit sizing method based on machine learning assisted global optimization | |
Aly | An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting | |
Li et al. | Training itself: Mixed-signal training acceleration for memristor-based neural network | |
Han et al. | An improved evolutionary extreme learning machine based on particle swarm optimization | |
Basu | Particle swarm optimization based goal-attainment method for dynamic economic emission dispatch | |
Tan et al. | Existence and global exponential stability of almost periodic solution for delayed competitive neural networks with discontinuous activations | |
Agami et al. | A neural network based dynamic forecasting model for Trend Impact Analysis | |
Stern et al. | Physical learning beyond the quasistatic limit | |
Mall et al. | Regression-based neural network training for the solution of ordinary differential equations | |
Soodi et al. | STATCOM Estimation Using Back‐Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks | |
Faraji et al. | A new macromodeling method based on deep gated recurrent unit regularized with Gaussian dropout for nonlinear circuits | |
Salem et al. | Parameters estimation of photovoltaic modules: comparison of ANN and ANFIS | |
Prakash et al. | Automatic load frequency control of six areas’ hybrid multi-generation power systems using neuro-fuzzy intelligent controller | |
Panda et al. | Fast and improved backpropagation learning of multi‐layer artificial neural network using adaptive activation function | |
Waheeb et al. | Nonlinear autoregressive moving-average (narma) time series forecasting using neural networks | |
Xin et al. | A-ELM⁎: Adaptive Distributed Extreme Learning Machine with MapReduce | |
Charoosaei et al. | High-order deep recurrent neural network with hybrid layers for modeling dynamic behavior of nonlinear high-frequency circuits | |
Zebhi et al. | Macromodeling of Nonlinear High-Speed Circuits Using Novel Hybrid Bidirectional High-Order Deep Recurrent Neural Network | |
Islam et al. | FPGA Implementation of Nerve Cell Using Izhikevich Neuronal Model As Spike Generator (SG) | |
Charoosaei et al. | High-Speed Nonlinear Circuit Macromodeling Using Hybrid-Module Clockwork Recurrent Neural Network | |
Cui et al. | Extreme learning machine based on cross entropy | |
Koh et al. | Pre-layout clock tree estimation and optimization using artificial neural network | |
Cao et al. | An adjoint dynamic neural network technique for exact sensitivities in nonlinear transient modeling and high-speed interconnect design | |
Müller et al. | Artificial neural networks for load flow and external equivalents studies | |
Gencer et al. | Design and validation of an artificial neural network based on analog circuits |