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119
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GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
15 years 5 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
106
Voted
NN
2000
Springer
128views Neural Networks» more  NN 2000»
15 years 1 months ago
A recurrent neural network for solving linear projection equations
Linear projection equations arise in many optimization problems and have important applications in science and engineering. In this paper, we present a recurrent neural network fo...
Youshen Xia, Jun Wang
IJCNN
2006
IEEE
15 years 7 months ago
Effective Training Methods for Function Localization Neural Networks
— Inspired by Hebb’s cell assembly theory about how the brain worked, we have developed a function localization neural network (FLNN). The main part of a FLNN is structurally t...
Takafumi Sasakawa, Jinglu Hu, Katsunori Isono, Kot...
NPL
2006
137views more  NPL 2006»
15 years 1 months ago
Minimal Structure of Self-Organizing HCMAC Neural Network Classifier
The authors previously proposed a self-organizing Hierarchical Cerebellar Model Articulation Controller (HCMAC) neural network containing a hierarchical GCMAC neural network and a ...
Chih-Ming Chen, Yung-Feng Lu, Chin-Ming Hong
JDCTA
2010
126views more  JDCTA 2010»
14 years 8 months ago
Continuous Neural Decoding Method Based on General Regression Neural Network
Neural decoding is an important task for understanding how the biological nervous system performs computation and communication. This paper introduces a novel continuous neural de...
Jianhua Dai, Xiaochun Liu, Shaomin Zhang, Huaijian...