Yanai et al., 1993 - Google Patents
A theory on a neural net with nonmonotone neuronsYanai et al., 1993
- Document ID
- 3310559376677306169
- Author
- Yanai H
- Amari S
- Publication year
- Publication venue
- IEEE International conference on Neural Networks
External Links
Snippet
A theoretical equation on dynamical processes on a neural net consisting of neurons with two-stage nonlinear dynamics is shown. The neurons have nonmonotone response characteristics when parameters are chosen as such. By the exact solution, the high …
- 210000002569 neurons 0 title abstract description 76
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
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen | A rapid supervised learning neural network for function interpolation and approximation | |
| De Veaux et al. | A comparison of two nonparametric estimation schemes: MARS and neural networks | |
| Jordan | Why the logistic function? A tutorial discussion on probabilities and neural networks | |
| Ponnapalli et al. | A formal selection and pruning algorithm for feedforward artificial neural network optimization | |
| Yanai et al. | Auto-associative memory with two-stage dynamics of nonmonotonic neurons | |
| Tan et al. | Adaptive fuzzy modeling of nonlinear dynamical systems | |
| Kak | On training feedforward neural networks | |
| Liu et al. | Hierarchical TS fuzzy system and its universal approximation | |
| Yam et al. | Determining initial weights of feedforward neural networks based on least squares method | |
| Yanai et al. | A theory on a neural net with nonmonotone neurons | |
| Mitaim et al. | Adaptive joint fuzzy sets for function approximation | |
| Wood et al. | A unifying framework for invariant pattern recognition | |
| Kohut et al. | Boolean neural networks | |
| Piuri et al. | Fault tolerance in neural networks: theoretical analysis and simulation results | |
| Watkin et al. | Learning multi-class classification problems | |
| Raudys et al. | First-order tree-type dependence between variables and classification performance | |
| Hwang et al. | Classification boundaries and gradients of trained multilayer perceptrons | |
| Otto et al. | Application of fuzzy neural network to spectrum identification | |
| Yeung et al. | Using a neuro-fuzzy technique to improve the clustering based on similarity | |
| Chen et al. | Neural network automata | |
| Yousefi’zadeh et al. | Neural network modeling of discrete time chaotic maps | |
| Stubberud et al. | Associative recall using a contraction operator | |
| Leung et al. | The behavior of forgetting learning in bidirectional associative memory | |
| Ludermir | Logical neural nets and distributed implementations of weighted regular languages | |
| WO1990015390A1 (en) | Parallel distributed processing network characterized by an information storage matrix |