Abstract: A pain inferring device for designing a shape giving a subject as little pain as possible when the subject touches the shape. The pain inferring device includes an input unit, an output unit, a main control unit, a learning storage unit, and a neural network. The neural network learns the relationship between the input value of shape data and the output value of the data on the degree of pain when a subject touches a shape. By the learning, an input/output function (namely, the coefficient of coupling of neurons in layers) representing the relationship between the input and output values is defined and stored in the learning storage unit. When the shape data is inputted through the input unit, the neural network infers the degree of pain by using the function stored in the learning storage unit.

Abstract: A pain inferring device for designing a shape giving a subject as little pain as possible when the subject touches the shape. The pain inferring device (10) includes an input unit (11), an output unit (12), a main control unit (13), a learning storage unit (14), and a neural network (15). The neutral network learns the relationship between the input value of shape data and the output value of the data on the degree of pain when a subject touches a shape. By the learning, an input/output function (namely, the coefficient of coupling of neurons in layers) representing the relationship between the input and output values is defined and stored in the learning storage unit (14). When the shape data is inputted through the input unit (11), the neural network (15) infers the degree of pain by using the function stored in the learning storage unit (11).