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Reinforcement learning for an ART-based fuzzy adaptive learning control network (vol 7, pg 709, 1996)

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1315 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 7, NO. 5 SEPTEMBER 1996

Extemal

reinforcement

signal

Corrections to "Reinforcement Learning for an

ART-Based Fuzzy Adaptive Learning Control Network"

Cheng-Jian Lin and Chin-Teng Lin

qtemal

'

-

rein orcement In the above paper,' the graphics for Figs. 3,

5,

and

6

were printed

incorrectly due to a publisher error. The correct figures an:

as

follows.

r ( t + 1)

'I

P O +

1)'

_ _ _ - - -

Predicted y2

4

signal

jl

( t )

s

(

t

+

1)

Action

21

"22 IH3 OH2 where

'23

Critic

Network

Fig. 5. The fuzzy reasoning process in the FALCON model.

Action

Network

Input

States

x

( t )

Fig. 6. The proposed RFALCON.

Fig. 3 . The proposed FALCON. Manuscript received July 1, 1996.

The authors are with the Department of Control Engineering, National

Publisher Item Identifier S 1045-9227(96)08055-1.

'

C.-J. Lin and C.-T. Lin, IEEE Trans. Neural Networks, vol. ' 7 , pp. 709-73 1, Chiao-Tung University, Hsinchu, Taiwan, R.O.C.

May 1996.

數據

Fig. 5. The fuzzy reasoning process in the FALCON model.

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