23、机器学习中的参数识别与强化学习网络性能提升

机器学习中的参数识别与强化学习网络性能提升

1. 新型参数识别方法

1.1 相关算子与梯度计算

首先定义了算子“⊙”，对于向量 (u = (u_1, u_2, \cdots, u_n) \in R^n) 和 (v = (v_1, v_2, \cdots, v_n) \in R^n)，有 (u \odot v = (u_1v_1, u_2v_2, \cdots, u_nv_n) \in R^n)。同时给出了 (c_i) 的计算公式：
[
c_i = (c_{i1}, c_{i2}, \cdots, c_{im}) =
\left(
\begin{array}{c}
\frac{\mu_{A_{i1}}(x_{j1})(1 + \beta\mu_{A_{i1}}(x_{j1}))e^{\beta\mu_{A_{i1}}(x_{j1})}}{\sum_{l = 1}^{m}\mu_{A_{il}}(x_{jl})e^{\beta\mu_{A_{il}}(x_{jl})}} - \frac{\beta\mu_{A_{i1}}(x_{j1})e^{\beta\mu_{A_{i1}}(x_{j1})}}{\sum_{l = 1}^{m}e^{\beta\mu_{A_{il}}(x_{jl})}} \
\frac{\mu_{A_{i2}}(x_{j2})(1 + \beta\mu_{A_{i2}}(x_{j2}))e^{\beta\mu_{A_{i2}}(x_{j2})}}{\sum_{l = 1}^{m}\mu_{A_{il}}(x_{jl})e^{\beta\mu_{A_{il}}(x_{jl})}} - \frac{\beta\mu_{A_{i