This study aims to examine the relationship between an aging society with a decreasing population and shopping behavior in a Takasaki City suburb based on a questionnaire survey. Due to an increase in elderly households, it has become difficult for many people to look after their aging parents. Going shopping has become particularly difficult for elderly people, especially in suburbs situated far from commercial facilities. As there is no public transportation, the only way to reach them is by car. Driving cars to supermarkets or convenience stores has led to a rise in the number of traffic accidents involving elderly people. It is, therefore, imperative that in the near future, elderly households are able to meet their daily shopping needs by walking instead of using cars. To create this walkable city, the government’s support is indispensable to relocate elderly households and provide the changes in lifestyle that a senior generation demands.
We have been developing a prediction code of discharge current using neural network for constructing auto-controlling system in Hall thrusters. The neural network is feedforward neural network, which consists of 5 layers with 100 neurons. We adopted backpropagation method to the network and updated weights by AdaGrad. We used training 25500 data sets that consists of operation condition (inner and outer coil current, xenon mass flow rate, discharge voltage and time) and discharge current. The code could predict unknown discharge current history within relative error 1% with three days. The relative error with 2250 training data sets remains less than 1% within eight hours calculation on a standard PC. Considering actual operation, it is necessary to make learning speed up.