改进EmBP：自组织图驱动的混合情感学习新算法提升性能

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本文主要探讨了两种新颖的混合情感学习算法，这些算法基于自组织图（Self-Organizing Map, SOM）在情绪识别领域的应用。情绪在人类决策过程中扮演关键角色，因此在构建人工智能系统时，如在人类行为模拟（如React）中，情感建模是至关重要的。阿德南·卡什曼提出的EmBP（情感反向传播）算法虽然曾取得了一些成功，但其情感输入值设计的局限性可能导致整体性能下降。针对EmBP的不足，研究人员提出了EmSOM（情感自组织映射神经网络），这是一种创新的算法。EmSOM通过考虑每个情感输入值与其关联的SOM块的关系来设计，这有助于更准确地反映情绪特征。此外，它还考虑到网络层次结构，进一步优化了情感表示，提高了学习效率。为了进一步提升模型性能，文章引入了稀疏在线SOM（SOR-SOM）算法，并将其整合到情感学习中，形成了Em-SOR-SOM混合模型。这个模型利用SOM的优势，使情感神经网络在实时性和精度上有了显著提升。作者对比了EmSOM、Em-SOR-SOM与SBP（简单情感反向传播）、EmBP和其他最新算法，通过ORL人脸数据库和三个基准信用数据集的实验，验证了新算法的有效性和效率。本文的研究旨在解决情感识别中的问题，通过结合自组织图和情感学习技术，提出了一种更为精确且高效的模型，对于提升人工智能系统的社会交互能力和决策支持具有重要意义。同时，作者强调了版权规定，指出研究论文受版权保护，仅限个人使用，并要求在分享或存档时遵循特定的条件。

with diversive curiosity (DCPROP) in their previously

published paper, by integrating the mechanism of diversive

curiosity with BP learning algorithm for solving its ‘‘ﬂat

spot’’ problem and for helping it escape from local minima.

Moreover, Ioannou et al. [6] have realized emotion

recognition through facial expression analysis based on a

neurofuzzy network. Shi et al. [7] have proposed a hier-

archical processed frame construction of artiﬁcial emotion

model for intelligent system according to the basis con-

clusion of emotional psychology. Bhotti et al. [8] have

presented a language-independent emotion recognition

system in their paper for the identiﬁcation of human

affective state in the speech signal. Kishore et al. [9] have

described a novel hybrid emotional neural network for

classiﬁcation of emotions from facial expressions.

Besides, Khanchandani et al. [10] have explored per-

formance analysis of multilayer perceptron neural network

and generalized feedfor ward neural network for detection

of seven human emotions, i.e., neutral, anger, boredom,

disgust, fear, happiness, and sadness, using speech signals.

Dai et al. [11] have used landmark and other acoustic

features to recognize different emotional states in speech.

riau et al. [12] have investigated how changes in the

functional magnetic resonance imaging signal during per-

ceptual decision making on facial stimuli covary with

individual differences in the ability to identify and com-

municate one’s emotional state.

In one of their published wor ks [3], Khashman presented

a modiﬁed backpropagation (BP) learning algorithm,

namely the emotional backpropagation (EmBP) learning

algorithm. Their new algorithm has additional emotional

weights that are updated using two additional emotional

parameters: anxiety and conﬁdence. Their EmBP neural

network was ﬁrstly implemented to a facial recognition

problem. And experimental results have show n that the

addition of the two novel emotional parameters improves

the performance of the neural networ k yielding higher

recognition rates and faster recognition speed.

Khashman et al. [13] have also applied their EmBP

algorithm to a blood cell-type identiﬁcation problem.

Experimental results showed that the additional emotional

parameters and weights improved the identiﬁcation rate as

well as the classiﬁcation speed. And they further applied

EmBP algorithm to a credit risk evaluation problem [14].

Their experimental results have suggested that both emo-

tional and conventional neural networks can be used

effectively for credit risk evaluations; however, the emo-

tional models outperform their conventional counterp arts

in decision-making speed and accuracy, thus making them

ideal for implementation in fast automatic processing of

credit applications.

However, in their work, the input values to the emo-

tional neurons of both hidden and output layers of the

model are identically assigned as the global average value

of the speciﬁc input image pattern under processing. Their

reasons for such an evaluation are mainly accor ding to a

statement of Baumgartner et al., ‘‘Most of the publishe d

neuroimaging papers examining emotional processes have

used visual stimuli in order to evoke emotions [15].’’

Therefore, the global average of the input pattern is fed into

the emotional neurons of hidden and output layers of the

model, and the resulting total potentials are used to cal-

culate the outputs of the two layers, respectively. Through

this pattern averaging, the author attempts to mimic the

tendency of human emotional judgments and preferences

to be based on general impressions rather than precise

details of the objects being perceived [3, 13, 14].

However, during our theoretical and experimental

studies to the EmBP algorithm [3, 13, 14], we found that

this assignment of emotional input values is not reasonable

and may not be enough desirable for the successful

implementation of the whole algorithm. Fo r a simple

counterexample, there might exist two input images, which

are observably different from each other, and belong to two

different persons, but their global average values may be

rather close to each other. In this kind of cases, the

implementation of EmBP might abate.

Enlightened by the above observations, we develop a

novel self-organizing map (SOM) [

16]-based emotional

(EmSOM) neural network learning algorithm of our own.

In the EmSOM algorithm, the input values to the emotional

neurons are not simply assigned as the global average value

of the input pattern. Instead, the unsupervised SOM

learning algorithm [16] is employed to compute these input

values. Speciﬁcally, ﬁrst , a SOM network associated with

the hidden layer of the model is generated taking all the

input patterns as its training data. Then, for a speciﬁc input

pattern under processing, the winni ng element of this

generated SOM is found out, and its average value of

weights with respect to its dimension is fed into the emo-

tional neuron of the hidden layer of the EmSO M model,

and the resulting total potential is used to compute the

output of the hidden layer.

Correspondingly, another SOM network associated with

the outpu t layer of EmSOM is generated taking all the

activation outputs from the hidden layer of EmSOM as its

training data. And then, accordingly, for a speciﬁc input

pattern under processing, the winni ng element of this

generated SOM is selected which is the closest to the

speciﬁc activation output vector of the hidden layer of

EmSOM. And then, its average value of weights with

respect to its dimension is fed into the emotional neuron of

the output layer of EmSOM, and the resulting total

potential is used to compute the ﬁnal output.

Our motivations behind the design of EmSOM algo-

rithm are as follows: Just as what we have mentioned

Neural Comput & Applic

123

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