Transcript 第九章模糊与神经网络倒车系统比较

```第九章 模糊与神经网络倒

Bart Kosko
Neural Networks and Fuzzy Systems

1。本章来源
2。倒车问题
3。Kosko的模糊控制模型
4。比较与分析
5。自适应模糊倒车
6。拖车问题
7。总结
Bart Kosko
Neural Networks and Fuzzy Systems

1。本章来源
Nguyan&Widrow 1989年 发表“The Truck Back-up, An Example
of Self-Learning in Neural Network”
Seong-Gon Kong&Bart kosko的与本章同名的论文在南加州大学

Bart Kosko
Neural Networks and Fuzzy Systems

2。倒车问题
0 ≤x ≤100
-90 ≤θ ≤ 270
-30 ≤ φ ≤30
θ正值代表顺时针方向旋转。 为减少计算量，将所

Bart Kosko
Neural Networks and Fuzzy Systems

3。Kosko的模糊控制器
3.1输入输出变量模糊集和隶属度

Bart Kosko
Neural Networks and Fuzzy Systems

3.2 FAM规则及控制面
LE
RB
1
PS
2
LC
CE
RC
RI
PM
PM
PB
PB
RU
NS
PS
PM
PB
PB
RV
NM
NS
PS
PM
PB
VE
NM
NM
18ZE
PM
PM
LV
NB
NM
NS
PS
PM
LU
NB
NB
NM
NS
PS
LB
NB
NB
NM
NM
NS
Bart Kosko
Neural Networks and Fuzzy Systems

3.3 系统仿真动态方程
Kokso：
x’=x+rcos(φ’)
y’=y+rsin(φ’)
φ’ = φ +θ
Nguyen&Widrow:
BP网络
Li-Xin Wang:
x’=x+rcos(φ’)+sin(θ)sin(φ)
y’=y+rsin(φ’) -sin(θ)cos(φ)
φ’ = φ-sin-1[2sin(θ)/b]
Bart Kosko
Neural Networks and Fuzzy Systems

3.4 相关最小FAM推理（Correlation-minimum FAM Inference Procedure)

o
o
i

i
i
, si )
i
p
 
 min( f

j 1
j
m o( j )
p
 m o(
j 1
j
)
Bart Kosko
Neural Networks and Fuzzy Systems

Bart Kosko
Neural Networks and Fuzzy Systems

3.5 Kosko控制器实验结果
X=20
Y=20
Φ=30
X=30
Y=10
Φ=220
X=30
Y=40
Φ=-10
Bart Kosko
Neural Networks and Fuzzy Systems

4. 分析与比较
4.1 综合情况

BP神经网络训练过程时间长，需几千个训练样本。

Bart Kosko
Neural Networks and Fuzzy Systems

4.2 容错能力分析

( f   ) 2  ( x f  x) 2  ( y f  y) 2

Bart Kosko
Neural Networks and Fuzzy Systems

Bart Kosko
Neural Networks and Fuzzy Systems

5. 自适应模糊倒车

Bart Kosko
Neural Networks and Fuzzy Systems

2230个训练矢量与

Bart Kosko
Neural Networks and Fuzzy Systems

DCL聚类结果如图。

Bart Kosko
Neural Networks and Fuzzy Systems

Bart Kosko
Neural Networks and Fuzzy Systems

Bart Kosko
Neural Networks and Fuzzy Systems

6. 拖车问题
Bart Kosko
Neural Networks and Fuzzy Systems

通过计算得到
Bart Kosko
Neural Networks and Fuzzy Systems

Bart Kosko
Neural Networks and Fuzzy Systems

7. 总结

Bart Kosko
Neural Networks and Fuzzy Systems
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