CIESC Journal ›› 2018, Vol. 69 ›› Issue (S1): 80-86.doi: 10.11949/j.issn.0438-1157.20171515

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Artificial affective neural networks with applications

WANG Xueliang, LI Hongguang   

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • Received:2017-11-14 Revised:2018-01-31 Online:2018-09-30 Published:2018-09-30


In order to improve the learning performance of neural networks with small amounts of samples so as to enhance the learning speed, a novel artificial affective neural network (AANN) is explicitly introduced in this paper. Traditional neural networks are integrated with non-fully connected affective neurons whose non-linear activation functions are different from traditional ones. Using two affective factors termed as “nervousness” and “confidence” to simulate the emotional changes of human brains, the cognitive ability of neural networks can be practically increased, which contributes to the speed of learning. A numerical example and the superheated steam temperature predictive control strategy of boilers concerning AANN applications are demonstrated, showing better performances compared with those of BP neural networks and support vector machine (SVM).

Key words: artificial affective neural network, emotion factor, predictive control, superheated steam temperature

CLC Number: 

  • TP27
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