Analysis and application of large chaotic neural networks
Grant-in-Aid for international Scientific Research
|Allocation Type||Single-year Grants|
|Research Institution||Tokyo Denki University|
HORIO Yoshihiko Tokyo Denki University Associate Professor, 工学部, 助教授 (60199544)
SUYAMA Ken The University of Tokyo Associate Professor, 電気工学科, 助教授
AIHARA Kazuyuki The University of Tokyo Associate Professor, 工学部, 助教授 (40167218)
KEN Suyama コロンビア大学, 電気工学科, 助教授
|Project Period (FY)
Completed(Fiscal Year 1996)
|Budget Amount *help
¥1,600,000 (Direct Cost : ¥1,600,000)
Fiscal Year 1996 : ¥1,600,000 (Direct Cost : ¥1,600,000)
|Keywords||Chaos / Neural Network / Complex System / Analog VLSI / Nonlinear System / Dynamical System|
A chaotic neuron model has been implemented as an integrated circuit (IC) form using switched-capacitor circuit technique. The model includes a relative refractoriness and an analog output function of the axon hill-lock which are important characteristics of biological neurons. The integrated chaotic neuron circuit has current input nodes in order to add many input currents easily. A current to voltage converter circuit was specially designed for this purpose.
Furthermore, a synaptic circuit has been integrated as an IC form in order to construct a large scale chaotic neural network by connecting many neuron circuits. Within the synaptic chip, many current-mode synapse circuits and analog memories for synaptic weights are integrated. The analog memories are periodically refreshed by a master current copier circuit.
By using the above neuron chips and synaptic chips, a small-scale but full-functional chaotic neural network has been constructed. The network characteristics have been tested using the following three basic problems ;
1.Dynamical Associative Memory : From experiments, a dynamical retrieval of memories like animals do has been confirmed.
2.Optimization Problems : As an example, a traveling salesman problem has been solved using the network. As a result, very high solving ability of the network has been confirmed.
3.Synchronization of Chaos : synchronous motion of some chaos in the network has been achieved.
The behavior of the implemented chaotic neural network system can be observed in real-time. Therefore, it is possible to analyze the process of information processing of a coupled chaotic system from the view point of dynamics theory or information theory by constructing a larger network. The expansion of the size of the network and construction of a multi-order parallel observation system are future problems.
Research Output (30results)