Supervised learning method for integrating information from several sensors-integration of inconsistent sensory inputs

In our previous work, we presented a sensory integrating system using several sets of neural networks and sensors. Each neural network recognizes inputs from corresponding sensors, the system integrates all outputs of the neural networks to get a high generalization ability. However, there are cases where the system fails to learn to discriminate some objects, if a part of the attributes of the object are shared by another class of objects. This is due to the fact that each neural network is independent from other networks. To solve the problem, we propose a new system which adaptively ignores a part of the sensory inputs which correspond to the shared attribute