scholarly journals Concepts Selection in Fuzzy Cognitive Map using Evolutionary Learning Algorithm based on Graph Theory Metrics

2017 ◽  
Author(s):  
Katarzyna Poczęta ◽  
Łukasz Kubus ◽  
Alexander Yastrebov
Keyword(s):  
Graph Theory ◽  
Learning Algorithm ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Evolutionary Learning

Fuzzy Cognitive Research on Factors Influencing Technological Innovation – From Path Dependence Perspective

2016 ◽  
Vol 20 (4) ◽  
pp. 543-553
Author(s):  
Jing Hu ◽  
◽  
Yong Zhang ◽  
Yilin Wang ◽  
Keyword(s):  
Technological Innovation ◽  
Path Dependence ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Computer Software ◽  
Feedback System ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Innovation Activity ◽  
Stable Conditions

This paper aims to establish a framework for evaluating technological innovation and to emphasize the important influence of path dependence on technological innovation. The fuzzy cognitive map (FCM) method is used to identify causal relationships among factors that influence technological innovation, and a FCM structural diagram for evaluating enterprise technological innovation is described. Meanwhile, a fuzzy feedback system for the evaluation of technological innovation, integrated with a nonlinear Hebbian learning algorithm, is established; dependence on expert opinions may be avoided through learning and practice using the cognitive map. Finally, using a computer software platform, a dynamic simulation of any complex index system can be realized. From this simulation, stable conditions can provide path references by which an enterprise engaging in technological innovation can improve the integrative efficiency and the overall effect of any realistic technological innovation activity.

scholarly journals A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Zhang ◽  
Xueying Zhang ◽  
Ying Sun
Keyword(s):  
Emotion Recognition ◽  
Learning Algorithm ◽  
Spectral Feature ◽  
Recognition System ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Speech Emotion Recognition ◽  
Feature Combination ◽  
Single Feature ◽  
The Relationship

Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps). Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%–20% better than those of single features. The proposed FCM learning algorithm generates 5%–20% performance improvement compared with traditional classification networks.

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