1. A neural network designing method comprising: a forming procedure causing a computer to form a RNN (Recurrent Neural Network) circuit to include a plurality of oscillating RNN circuits configured to output natural oscillations, and an adding circuit configured to obtain a sum of outputs of the plurality of oscillating RNN circuits; and a computing procedure causing the computer to input discrete data to the plurality of oscillating RNN circuits, in order to compute a fitting curve with respect to the discrete data output from the adding circuit, wherein the computing procedure includes computing and storing in a storage unit a 0 ,a 1 ,. .. , a 2n-1 ,b 1 ,. .. , b 2n-1 represented by the following formulas from the discrete data y 0 =0, y 1 ,. .. , y 2n-1 , y 2n =0, a 0 ⁢ = 1 2 ⁢ n ⁢ ∑ i = 1 2 ⁢ n - 1 ⁢ y i = 1 2 ⁢ n ⁢ ∑ i = 0 2 ⁢ n ⁢ y i a q = ψ ⁡ ( q 2 ⁢ n ) 4 ⁢ n ⁢ ⁢ π 2 ⁢ ⁢ ∑ i = 1 2 ⁢ n - 1 ⁢ y i ⁡ ( - cos ⁢ ⁢ ( i - 1 ) ⁢ q ⁢ ⁢ π n + 2 ⁢ ⁢ cos ⁢ ⁢ i ⁢ ⁢ q ⁢ ⁢ π n - cos ⁢ ⁢ ( i + 1 ) ⁢ q ⁢ ⁢ π n ) q = 1 , 2 ⁢ ⁢ … ⁢ ⁢ 2 ⁢ n - 1 b q = ψ ⁡ ( q 2 ⁢ n ) 4 ⁢ n ⁢ ⁢ π 2 ⁢ ∑ i = 1 2 ⁢ n - 1 ⁢ y i ⁡ ( - sin ⁢ ⁢ ( i - 1 ) ⁢ q ⁢ ⁢ π n + 2 ⁢ sin ⁢ ⁢ i ⁢ ⁢ q ⁢ ⁢ π n - sin ⁢ ⁢ ( i + 1 ) ⁢ q ⁢ ⁢ π n ) q = 1 , 2 ⁢ ⁢ … ⁢ ⁢ 2 ⁢ n - 1 ψ ⁡ ( x ) = ⅆ 2 ⅆ x 2 ⁢ log ⁢ ⁢ Γ ⁡ ( x ) where ψ(x) denotes a trigamma function that is defined by a second derivative of a logarithm of a gamma function, and a 0 , a 1 ,. .. , a 2n-1 , b 1 ,. .. , b 2n-1 are fixed values independent of the time t, and computing the fitting curve defined by the following formula, using the fixed values a 0 ,a 1 ,. .. , a 2n-l , b 1 ,. .. , b 2n-1 , y ⁡ ( t ) = a 0 + ∑ q = 1 2 ⁢ n - 1 ⁢ a q ⁢ cos ⁢ ⁢ q ⁢ ⁢ π n ⁢ t + ∑ q = 1 2 ⁢ n - 1 ⁢ b q ⁢ sin ⁢ ⁢ q ⁢ ⁢ π n ⁢ t where cos (qπt/n) and sin(qπt/n) are oscillating functions output from the plurality of oscillating RNN circuits.