EmSOM与Em-SOR-SOM：混合情感自组织图学习算法

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"Adnan Khashman等人提出的情感反向传播(EmBP)学习算法在模式识别任务中表现出色，但其情感输入值的设计存在不足。为了解决这个问题，本文提出了基于自组织图的情感神经网络(EmSOM)学习算法，该算法的情感输入值基于SOM块的关联确定，同时考虑了网络的层次结构，从而弥补了EmBP的缺陷。此外，通过整合稀疏在线SOM(SOR-SOM)算法，构建了基于稀疏在线关系的SOM情感神经网络(Em-SOR-SOM)混合模型，进一步提升了模型的识别性能。通过在ORL人脸数据库和三个信用数据集上的实验，EmSOM和Em-SOR-SOM算法的效能和效率得到了数值验证，并与SBP、EmBP和其他最新算法进行了对比。" 本文深入探讨了情感在决策过程中的重要性，并介绍了两种新颖的混合情感学习算法。首先，EmBP算法被指出在情感输入设计上的局限性，这可能导致其在实际应用中的效果不佳。然后，EmSOM算法作为改进方案被提出，它的关键在于利用SOM的自组织特性来确定情感输入值，同时考虑了网络的层次结构，这使得情感信息的处理更为合理。EmSOM的创新之处在于它能够更好地模拟人类情感对决策的影响。接着，为了进一步提升情感学习的性能，作者引入了SOR-SOM算法，创建了Em-SOR-SOM混合模型。SOR-SOM以其稀疏性和在线学习能力而著称，将其与情感学习相结合，旨在优化模型的识别能力。实验结果表明，EmSOM和Em-SOR-SOM在ORL人脸数据库和信用评估数据集上的表现优于其他算法，包括SBP和原始的EmBP，这证明了这两种新算法的有效性和效率。这篇研究论文提出了基于自组织图的混合情感学习算法，旨在解决现有情感学习算法的局限性，通过结合SOM和SOR-SOM的优势，提高了情感信息在模型中的处理能力和识别性能。这些方法对于情感计算、模式识别以及决策支持系统等领域具有重要的理论和实践意义。

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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