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when no examples of both faults occur- of Southern California (USC) uses the Hong Kong University of Science ring together are included in the train- Hebbian type of correlation for mem- and Technology, there is work on neting set. The main conclusion is that the ory storage. However, by using works whose processing elements are spectral representation provides the Householder encoding, the memory quadratic functions and work on mapbest recognition performance and that capacity of the BAM is greatly improved ping multilayer attributed graphs onto the primary discriminatory informa- over the conventional BAM. Also at a neocognitron network. tion for distinguishing the two types of faults (impeller unbalance and cracked

Table 1. Breakdown of Authors and impeller) is contained in the first three

Delegates by Country harmonics. This work is relevant to the Navy's research program on helicopter

Country

Authors Delegates gearbox fault diagnosis.

Australia

25

33 CHINA

Austria

2

2 Belgium

6

6 The neural network research in China Brazil

2

2 is also spread out in many universities. Bulgaria

1 The work is very much application Canada

16

14 oriented, as can be seen by the list of Chile

1

1 topics and locations in Table 3.

China

27

16 Of particular interest to the Navy is Czechoslovakia

1 the work of Jungang Xu, Zhong Wang,

Finland

4

5 and Youan Ke at the Beijing Institute France

12

11 of Technology on optimum frequency Germany

20

16 selection for radar target classification Greece

1 by a neural network. The radar cross Hong Kong

10

11 sections at multiple frequencies are used India

11

4 as the inputs to a backpropagation neural Indonesia

1 network for radar target classification. Ireland

1 The frequencies corresponding to the Israel

2 input nodes that have maximum sensi- Italy

11

11 tivities are selected as optimum fre- Japan

97

125 quencies needed for classification. The Korea

17

19 method is applied successfully in clas- Mexico

1 sifying simple radar targets in an Malaysia

1 Anechoic Chamber. Obviously this Netherlands

4

4 result has significance in automatic radar New Zealand

3

2 target classifications for the Navy. Norway

1 Singapore

26

103 HONG KONG South Africa

1 Spain

2

2 Research is concentrated in the three Sweden

1 big universities in Hong Kong. At the Switzerland

2

2 Hong Kong University of Science and Taiwan

13

11 Technology, there is work on the auto- Thailand

1

1 matic determination of multilayer feed- Turkey

1

1 forward network size for supervised United Arab Emirate

1

1 learning and work on using the United Kingdom

28

19 Householder encoding algorithm for U.S.A.

107

91 discrete bidirectional associative U.S.S.R.

4

1 memory (BAM). The original BAM as Yugoslavia

2

2 proposed by Bart Kosko of the University

N N

2

Table 2.

Research Topics and Locations in Australia

Topic

Location

On-line identification of nonlinear systems
Time series analysis
Hybrid systems
Higher order neural nets
Rotating machine fault diagnosis
Learning in feedforward networks
Texture segmentation
Dynamic channel assignment
Classification of intracardiac electrocardiograms
Probabilistic neural network
Reinforcement learning
Object recognition for robots
Kohonen algorithm on transputers
Information retrieval
Visual reconstruction networks
Adaptive quadratic neural net
Autoassociative network
Network of exponential neurons
Robust networks
Self-organizing network for object recognition
Genetic algorithms
Unsupervised learning for neural trees
Kolmogorov representation theorem

Univ. of Western Australia
Univ. of Melbourne
Swinburne Inst. of Tech.
Telecom Research Labs
Royal Melbourne Inst. of Tech.
Univ. of Queensland
Monash Univ.
Univ. of Melbourne
Univ. of Sydney
Univ. of Western Australia
Univ. of Western Australia
BHP Research, Melbourne Lab
Univ. of Western Australia
Univ. of New South Wales
Telecom Research Labs
Univ. of Western Australia
Griffith Univ.
Queensland Univ. of Tech.
Univ. of New South Wales
Univ. of Melbourne
Univ. of Western Australia
Telecom Research Labs
Telecom Research Labs

At the Chinese University of Hong Basis Function (RBF) classifier of traditionally they have been very strong Kong, the neural network research Thomas Poggio.

in learning automatons, but only a small efforts are spearheaded by Professor At City Polytechnic of Hong Kong, number of papers were presented at Lai-Wan Chan, who is a graduate of a new Hopfield type of training algo- this conference. the Imperial College in London. They rithm has been developed whereby the At the Indian Institute of Science, a have developed a novel learning algo- orthogonally coded memories are great deal of work is devoted to the rithm for a recurrent backpropagation obtained iteratively. Also a structured study of bidirectional associative network and a system for detecting three- backpropagation network has been memories (BAM). The extended hyperdimensional (3-D) motion from a developed that adapts to the problem. cube, as well as the INMOS transputersequence of image frames. At the Clearly these are initial efforts in apply

Clearly these are initial efforts in apply- based computer system, is used to Chinese University, BAM is studied ing neural network technology. simulate the parallelism in BAM. The from the viewpoint of match filtering.

Fokker-Planck equation is also used to Sufficient conditions are found for INDIA

study the dynamics of associative stability and attractivity for BAM net

memories. The Hopfield-based model works. Also there is work on neural Research appears to be centered at for distributed representation of objects networks that learn the decision bound- the Indian Institute of Science and the and for use as content addressable aries with nonlinear clustering, similar Center for Artificial Intelligence and memory is also investigated. There is to the Reduced Coulomb Energy (RCE) Robotics. It seems that there should be also research work on genetic algomodel of Leon Cooper and the Radial a lot more work going on in India because rithms and learning automata for pat

tern recognition.

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Associative memory
Self-improving associative network
Adaptive pattern recognition
Transform domain backpropagation algorithm
Laterally inhibitory neural network
Multilayer network with dynamic neurons
Recall in multilayer perceptron'network
New neural network architecture
Radar target classification
Character recognition using holographic memory
Local minima in backpropagation error surface
Job-shop scheduling
Object oriented neural network language
Unstructured economic decision process
Adaptive predictor for nonlinear dynamics
Speaker independent syllable recognition
Absolute stability of Hopfield nets
Short time speech recognition
Bounds on the approximation capacity
Hopfield-Tank model for solving TSP
Weighted associative memory
Fault tolerance in self-organizing nets
Complexity of learning algorithms
Speaker recognition

Tsinghua Univ.
Zhejiang Univ.
Nanjing Aeronautical Inst.
Jiao Tong Univ.
Tsinghua Univ.
South China Univ. of Tech.
Inst. of Electronics
Inst. of Electronics
Beijing Inst. of Tech.
South China Univ. of Tech.
Southeast Univ.
Tsinghua Univ.
Changsha Inst. of Tech.
Inst. of Auto. Academia Sinica
Univ. of Sci. & Tech. of China
Beijing Inst. of Tech.
Beijing Univ. of Aeronautics
South China Univ.
South China Univ. of Tech.
Beijing Univ. of Post & Telecom.
Zhejiang Univ.
Southeast Univ.
Tsinghua Univ.
Peking Univ.

of particular interest to the Navy is number of researchers involved in neural Top-down views of three different airthe work of P.Y. Mundkur and networks is close to a thousand. The craft (DC-10, Phantom, and MiG21) at U.B. Desai at the Indian Institute of papers, close to a hundred, presented different azimuth and roll angles are Technology on automatic target recog- at this conference are but a small por- used for training the network. The trainnition. A sequence of images taken tion of the neural network work that is ing set consists of 216 images (72 images from high altitude reconnaissance flights going on in Japan. Table4 is a list of the per aircraft at 10° roll and azimuth is used as input to the network. The topics of research at various universi- angles). Two multilayer feedforward network consists of two cascaded ties and industry.

neural nets are used, one for classifying modules, a multilayer perceptron net

the type of aircraft and another for work followed by a modified Maxnet, KOREA

estimating the orientation angle. Out to provide translation-invariant recog

of the three aircraft types, correct clasnition of the targets in clutter. The Most of the neural network research sification approaches the 98.6% level, network is partitioned into subnets and is centered at the Korea Advanced and the accuracy of estimating the oria backpropagation algorithm is used to Institute of Science and Technology, entation is at 89.4%. Of course, these train the subnets. Preliminary results although there are a few efforts at the performance levels are extremely good show promise, but a lot more research other universities and industry. Of for such a small number of different needs to be done yet.

particular interest to the Navy is the aircraft types. This work has implica

work of Dae-Young Yim, Sung-Il Chien, tions in military automatic target recogJAPAN

and Hyun Son at Kyungpook National nition as well as in commercial avia

University on multiclass 3-D identifi- tion. Table 5 is a listing of the location Japan clearly is the most technolog- cation and orientation estimation using of different neural network research in ically advanced country in Asia. The multilayer feedforward neural networks. Korea.

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Hebbian type associative memory
Multilayer network for character recognition
Adaptive input field neural network
Hybrid neuromorphic and symbolic control
Hybrid control of robotic manipulator
Sequential network for speech recognition
Fuzzy logic neural network
Neuron with a center
Error correction learning in three-layer network
Inverted pendulum problem
Adaptive decision feedback equalization
Optimal control with recurrent network
Three-layer backpropagation for pattern recognition
Hierarchical Markov Random Field model
Finite size multilayer net for polynomials
Models of the cerebellum
Fuzziness in 3-D surface depth perception
Recognition of facial expressions
Network model for color blindness
Model for conscious and unconscious processing
Time delay discrete neural network
Multilayer network with quantizer neurons
Inverse modeling of dynamical systems
Fuzzy training algorithm for phoneme recognition
Memory based artificial neural network
Learning rule using difference approximation
Spatial inhibition and local association
Pattern classification for remote sensing
Moment invariants for katakana recognition
Road segment extraction from maps
Rule evaluating neural networks
Feature selection for recognition
Piecewise linear higher order network
Learning process of recurrent networks
Automatic determination of association units
Estimation of a posteriori probability
Asymptotic behavior of simulated annealing
Global suppression of spurious states
Automatic gray level adjustments
Multiply descent cost competitive learning
Regularization vision chips
CombNET-II for written digit classification
Rotation invariant neural pattern recognition
Hierarchical intelligent control
Feedforward network for robot motion control
Visual tracking of robot manipulator
Finite times of search in multilayer networks
Synthesizing networks for pattern recognition
Elimination of local minima in backpropagation
Neural sequential associator for prediction
Speaker independent 1000 word recognition
Car detection system using neocognitrons
Limb function discrimination using EMG signals
Feedforward control based on inverse systems
Learning scheme to improve speed of convergence
Quasi-symmetric logic networks
Ultrasonic 3-D visual sensor
Space perception model
Human hand position control learning

Keio Univ.
Nagoya Univ.
NEC Corp.
Risarazu National Tech. College
Kisarazu National Tech. College
Kyoto Inst. of Tech.
Hosei Univ.
NTT Commun. Switching Lab
Kyushu Inst. of Tech.
Kyushu Inst. of Tech.
ATR Research Lab
Toshiba Corp.
Nagoya Univ.
ATR Auditory & Vision Lab
Toyohashi Univ. of Tech.
ATR Auditory & Vision Lab
Inst. of Physical & Chem. Res.
Sci. Univ. of Tokyo
Toyohashi Univ. of Tech.
Matsushita Res. Inst. of Tokyo
Nagoya Univ.
Matsushita Electric
Fujitsu Lab
NTT Human Interface Lab
Hiroshima Univ.
Kansai Univ.
Tokyo Univ. of Agric. & Tech.
Univ. of Tokushima
Tokyo Inst. of Tech.
Univ. of Tsukuba
Toshiba ULSI Res. Center
Yokogawa Electric Corp.
Univ. of Tokyo
Nagoya Univ.
Fukui Univ.
Toyohashi Univ. of Tech.
Hitachi Ltd.
Hitachi Ltd.
Toshiba Corp.
Ibaraki Univ.
Yokogawa Electric Corp.
Nagoya Inst. of Tech.
Univ. of Tokushima
National Kisarazu Tech. College
Nippon Steel Corp.
Univ. of Tokyo
Tokyo Inst. of Tech.
Hitachi Ltd.
Kyushu Inst. of Tech.
Hitachi Ltd.
Nagoya Inst. of Tech.
Chiba Inst. of Tech.
Nagoya Univ.
Mitsubishi Heavy Industries Ltd.
Tokyo Inst. of Tech.
Ryukoku Univ.
Ricoh Co. Ltd.
Univ. of Tokyo
Univ. of Tokyo

(continued)

[blocks in formation]

Receptive field network for kanji recognition
Extension of backpropagation algorithm
Regression analysis with inverse model
Face graph method for fuzzy neural networks
Bidirectional optical neural net
Basin size in autoassociative memory
Learning algorithm based on information theory
Recursive neural system in a tree like structure
Reduction of precision in learning
Neural network based adaptive control system
Wafer scale LSIs with 1152 digital neurons
Low-bit learning algorithm for pattern recognition
Cross-coupled Hopfield network
Neuromorphic sensing and control
Controller for autonomous underwater robot
Trajectory generation for biped robot
Inverse kinematic calculations
Self-learning robot vision system
NN/II network for pattern recognition
Time warping network for phoneme recognition
Adaptive neural model reference structure
Structure detection by neural sequence associator
Neural searchlight processor
Cluster formation in random neural network
Parallel ASIC VLSI neurocomputer
Inducing algorithm for LTP in hippocampus
Temporal association in symmetric networks
Dynamics in chaotic neural networks
T-model neural network with learning ability
Capabilities of three-layer network
Parallel algorithm for simulated annealing

lving the four color mapping prob em
Solving the dynamic traveling salesman problem
Broadcasting in multihop packet radio network

Table 5.

Research Topics and Locations in Korea

Topic

Iterative autoassociative memory
ARMA model time series modeling
Parallel Boltzmann machine
Benchmarks for learning algorithms
3-D aircraft orientation identification
Image parameter estimation by error propagation
Hopfield network for self-tuning control
Functional approximation
Hopfield network for obstacle avoidance
Optical implementation for BAM
Perceptrons for image recognition
Hidden node reduction techniques
Distributed memory multiprocessors
Neural network for systolic design
Nearest neighbor classifier
Weight value initializations

Location

Sharp Corp.
Univ. of Osaka Prefecture
Univ. of Osaka Prefecture
Nippondenso Co. Ltd.
NTT Transmission System Lab
Kyoto Univ.
NTT Human Interface Lab
Univ. of Tokyo
Matsushita Electric
Yokohama National Univ.
Hitachi VLSI Engineering Corp.
Kanazawa Univ.
Kobe Univ.
National Kisarazu Tech. College
Univ. of Tokyo
Kobe Univ.
Hitachi Ltd.
Waseda Univ.
Kyoto Univ.
NTT Human Interface Lab
Univ. of Tokushima
Hitachi Ltd.
Fukui Univ.
Kinki Univ.
Toshiba Corp.
Tamagawa Univ.
Tokyo Univ. of Agric. & Tech.
Tokyo Denki Univ.
Telecom Systems, Inc.
Sony Co., Ltd.
Univ. of Osaka Prefecture

yota Tech. Inst.
NTT Human Interface Lab
Keio Univ.

Location

Kyungpook National Univ.
Korea Adv. Inst. of Sci. & Tech.
Korea Adv. Inst. of Sci. & Tech.
Pohang Inst. of Sci. & Tech.
Kyungpook National Univ.
Korea Adv. Inst. of Sci. & Tech.
Yonsei Univ.
Korea Adv. Inst. of Sci. & Tech.
Kwangwoon Univ.
Korea Adv. Inst. of Sci. & Tech.
Pohang Inst. of Sci. & Tech.
Electronics & Telecomm Res. Inst.
Korea Adv. Inst. of Sci. & Tech.
Korea Adv. Inst. of Sci. & Tech.
POSTECH
Korea Adv. Inst. of Sci. & Tech.

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