Adjustability of Neural Networks with Variant Connection Weights for Obstacle Avoidance in an Intelligent Wheelchair
Toshihiko Yasuda*, Hajime Tanaka*, Kazushi Nakamura**,
and Katsuyuki Tanaka*
*Dept. of Mechanical System Engineering, The University of Shiga Prefecture, 2500 Hassaka-cho, Hikone, Shiga 522-8533, Japan
**Dai Nippon Printing Co., Ltd., 1-1-1 Ichigaya, Kagacho, Shinjuku-ku, Tokyo 162-8001, Japan
We have been studying electrically powered wheelchair operation to make electrically powered wheelchair intelligent and to develop a mobility aid for those who find it difficult or impossible to use conventional electrically powered wheelchairs. Some of the prototypes we have developed use neural networks providing obstacle avoidance. In previous research, we found that by varying neural network connection weight based on obstacles in the wheelchair’s vicinity and its run state, obstacle avoidance is improved. In this research, we discuss the adjustability of neural networks with variant connection weight based on numerical studies.
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