Abstract:

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

摘要：

为了提高神经网络的学习速度和神经网络小样本的学习性能，提出了一种人工情感神经网络（artificial affective neural network，AANN），在普通神经网络中加入了非全连接的情感神经元，使用与传统神经元不同的非线性激励函数，利用"紧张"与"自信"两个情感因子模拟人类学习的情感变化，可以提高神经网络对于对象的认知能力，进而提高学习速度。为了验证模型的有效性，分别采用数值实例和锅炉过热蒸汽温度预测控制进行了实例研究，结果表明，与BP神经网络以及支持向量机（SVM）相比，AANN对于小样本对象具有更好的辨识能力。

关键词: 人工情感神经网络, 情感因子, 预测控制, 过热蒸汽温度