Contents of Volume 30 (2020)

1/2020

  • [1] Gitto L., Massini G., Mennini F.S., Mento C., Buscema P.M. (Italy)
    Affective symptoms and postural abnormalities as predictors of headache: an application of artificial neural networks , pp. 1-26

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2020.30.001

    Abstract: Chronic headache is a major liability in the individuals' quality of life. Identifying in advance the main features common to patients with headache may allow planning a preventive strategy of intervention. An artificial neural network model (Auto Contractive Maps { AutoCM), aimed at analyzing the correlations between patients' characteristics, affective symptoms and posture indicators has been developed in this paper. Patients suffering from chronic headache were observed at a neurological centre in Sicily (Italy). Headache and affective states were measured using the Profile of Mood States (POMS), the Beck Depression Inventory (BDI), the Toronto Alexithymia Scale (TAS-20) and the Repression Scale. Postural evaluations were carried through a stabilometric platform. The method of analysis selected allowed to reconstruct some records that were missing, through a Recirculation AutoAssociative Neural Network, and to obtain sound results. The results showed how some items from TAS-20, Repression and POMS were closely linked. The postural abnormalities were correlated primarily with repression features. The highest scores of the POMS were correlated with the items of the BDI. The results obtained lead to interesting remarks about the common traits to patients with headache. The main conclusion lies in the potentialities offered by the new methodology applied, that may contribute, overall, to a better understanding of the complexity of chronic diseases, where many factors concur to define patients' health conditions.

  • [2] Hu J., Cao S., Xu C., Yao J., Xie Z. (China)
    High-accuracy motion control of a motor servo system with dead-zone based on a single hidden layer neural network , pp. 27-44

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2020.30.002

    Abstract: There always exist parametric uncertainties, bounded disturbances and some other unknown nonlinearities such as the input dead-zone in physical motor servo systems, which can degrade the system's control performance. In this paper, a composite control strategy is proposed for high-accuracy motion control of a torque-controlled motor servo system with dead-zone. A smooth and continuous mathematical model is used to provide an approximate inverse transformation of the input-output dead-zone needed for feedback linearization. A single-layer neural network capable of on-line learning is designed to compensate for the inversion error, which comes from the approximate inversion. A stable weights adaption law for the on-line neural network is derived. In addition, a parameter adaptation law is also derived for handling the parametric uncertainty, and a nonlinear robust feedback term is designed to inhibit the in uence of the imperfect modeling, compensation error or other disturbances. Lyapunov theorem is used to prove the stability of the proposed control algorithm with the weights and parameters adaptation law. Extensive comparative simulation results are used to illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

  • [3] Moos P., Novák M., Votruba Z. (CZ)
    Parametric sensitivity in decision making process, pp. 45-53

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2020.30.003

    Abstract: This paper introduces a possibility of application of parametric sensitivity appearing in processes of decision making in systems represented by the general production functions of the Tinbergen type, depending on information content, information flow and qualification of human resources. The so-called parametric sensitivity considering the information content I as an ordering parameter, dependent on the information flow , applied on production function. The theory of production function describes the relation between physical outputs of a production process and physical inputs, i.e. factors of production. Finally, the influence of knowledge in information content I, leading to correct decision, is demonstrated through the parametric sensitivity concept. For this invention, J. Tinbergen and R. Frisch achieved in 1969 the "Nobel price of the Swedish National Bank". Besides, the production functions theory surprisingly represents also a tool for finding the reasons of living bodies behavior.

  • [4] Svítek M. (CZ)
    Wave composition rules in quantum system theory, pp. 55-64

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2020.30.004

    Abstract: The paper presents the new approach to wave composition rules for advanced modeling of soft systems in quantum system theory. Firstly, the interpretation of phase parameters is given. The phase parameters are essential to specify the mathematical operations assigned to different relations among subsystems, e.g. co-operation, connection, co-existence, competition. Using wave composition rules, we are able to create more complex and sophisticated quantum circuits. We present the application of methodology on three illustrative examples.

  • [5] Dembani R., Zheng W., Sun M., Nooruddin (China)
    Unsupervised facial expression detection using genetic algorithm, pp. 65-75

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2020.30.005

    Abstract: Interpersonal communication can be done by understanding the clues of facial expressions. As its importance increase in behavior and clinical studies, so automatic detection of facial expressions is an open research area for the last few decades. Efforts of expression detection by a human being are easy and effective but the machine needs some more understanding. This paper proposes a face expression clustering using a genetic algorithm. Image get convert into binary format for finding the related cluster selection in different phases of genetic algorithm. Proposed work has utilized a modified teacher learning-based optimization algorithm where the population gets updated in each phase to get the best representative features. A real dataset of facial expression was used in this work. A comparison of the proposed model was done with existing models on different evaluation parameters. It was obtained that the proposed work has improved precision, recall, the accuracy of facial expression identification without any training.