```julia
using Symbolics
using LinearAlgebra
@variables x1, x2, x3, α

x = [x1, x2, x3]

"""ソフト正規化関数"""
function h(γ, α)
    1/γ * log(exp(α*γ) + exp(-α*γ))
end

"""ソフトマックス関数"""
function θ(v, α)
    v / h(norm(v), α)
end

function Φ_tilde_2a(x, η=α)
    h(norm(x), η)
end


#Dx = Differential(x)
Dx = Symbolics.gradient(Φ_tilde_2a, x)

#dx = Dx(Φ_tilde_2a(x,α))

#SymbolicUtils.simplify(dx)


```


\begin{equation}
\left[
\begin{array}{c}
0 \\
0 \\
0 \\
\end{array}
\right]
\end{equation}



6b56efd218ca95d0f2bab1fd2db9dc6099cd214c
ipynb
Jupyter Notebook
misc/symbols_test.ipynb
YoshimitsuMatsutaIe/manipulator_dynamics
587b3cedddd07c2aa09d1195289b0c312e0fc749
MIT
misc/symbols_test.ipynb
YoshimitsuMatsutaIe/manipulator_dynamics
587b3cedddd07c2aa09d1195289b0c312e0fc749
MIT
misc/symbols_test.ipynb
YoshimitsuMatsutaIe/manipulator_dynamics
587b3cedddd07c2aa09d1195289b0c312e0fc749
MIT
Qwen/Qwen-72B
1. YES
2. YES