|Budget Amount *help
¥4,500,000 (Direct Cost : ¥4,500,000)
Fiscal Year 2000 : ¥1,400,000 (Direct Cost : ¥1,400,000)
Fiscal Year 1999 : ¥1,500,000 (Direct Cost : ¥1,500,000)
Fiscal Year 1998 : ¥1,600,000 (Direct Cost : ¥1,600,000)
We have chosen the self-organizing map (SOM) as the target network in the neural networks, and evaluated its autonomous fault repairing capability quantitatively. Especially, in the project, we have proposed a defect model in which the defective neurons output arbitrary stuck values. From the analysis of the model, the following facts are shown (proved).
1) The SOM can repair the defective neurons autonomously, if the defective neurons'outputs are larger than the critical stuck output, which is derived from the proposed defect model.
2) The new criteria "critical stuck output" can be used widely even in the real applications such as image compression and face image recognition.
Furthermore, in the project, we have also evaluated fault tolerance of the evolutionary algorithm (genetic algorithms, genetic program etc.) because the high fault tolerance can be also expected not only in the neural networks but also other algorithms based on the biological information processing. In order to evaluate the fault tolerance, we have carried out fault injection experiments using simulation programs and the prototype machine constructed based on reconfigurable LSI (FPGA). From the experimental results, it has been shown that the hardware based on the evolutionary algorithms also has high fault tolerance and graceful degradation against defective circuits
From wll those experimental results we have show that neural network LSIs and evolutionary algorithms-based LSIs has high fault tolerance and they can repair the defective circuits autonomously.