An adaptive learning algorithm for a neuro-fuzzy network. Ed. by B. Reusch "Computational Intelligence. Theory and Applications

Abstract: In different fields a conception of granules is applied both as a group of elements defined by interna

The Vigilance Decrement in Executive Function Is Attenuated When Individual Chronotypes Perform at Their Optimal Time of Day

Time of day modulates our cognitive functions, especially those related to executive control, such as the ability to inhibit inappropriate responses. However, the impact of individual differences in time of day preferences (i.e. morning vs. evening chronotype) had not been considered by most studies. It was also unclear whether the vigilance decrement (impaired performance with time on task) depends on both time of day and chronotype. In this study, morning-type and evening-type participants performed a task measuring vigilance and response inhibition (the Sustained Attention to Response Task, SART) in morning and evening sessions. The results showed that the vigilance decrement in inhibitory performance was accentuated at non-optimal as compared to optimal times of day. In the morning-type group, inhibition performance decreased linearly with time on task only in the evening session, whereas in the morning session it remained more accurate and stable over time. In contrast, inhibition performance in the evening-type group showed a linear vigilance decrement in the morning session, whereas in the evening session the vigilance decrement was attenuated, following a quadratic trend. Our findings imply that the negative effects of time on task in executive control can be prevented by scheduling cognitive tasks at the optimal time of day according to specific circadian profiles of individuals. Therefore, time of day and chronotype influences should be considered in research and clinical studies as well as real-word situations demanding executive control for response inhibition.This work was supported by the Spanish Ministerio de Ciencia e Innovación (Ramón y Cajal programme: RYC-2007-00296 and PLAN NACIONAL de I+D+i: PSI2010-15399) and Junta de Andalucía (SEJ-3054)

Differential impact in young and older individuals of blue-enriched white light on circadian physiology and alertness during sustained wakefulness

We tested the effect of different lights as a countermeasure against sleep-loss decrements in alertness, melatonin and cortisol profile, skin temperature and wrist motor activity in healthy young and older volunteers under extendend wakefulness. 26 young [mean (SE): 25.0 (0.6) y)] and 12 older participants [(mean (SE): 63.6 (1.3) y)] underwent 40-h of sustained wakefulness during 3 balanced crossover segments, once under dim light (DL: 8 lx), and once under either white light (WL: 250 lx, 2,800 K) or blue-enriched white light (BL: 250 lx, 9,000 K) exposure. Subjective sleepiness, melatonin and cortisol were assessed hourly. Skin temperature and wrist motor activity were continuously recorded. WL and BL induced an alerting response in both the older (p = 0.005) and the young participants (p = 0.021). The evening rise in melatonin was attentuated under both WL and BL only in the young. Cortisol levels were increased and activity levels decreased in the older compared to the young only under BL (p = 0.0003). Compared to the young, both proximal and distal skin temperatures were lower in older participants under all lighting conditions. Thus the color temperature of normal intensity lighting may have differential effects on circadian physiology in young and older individuals. © 2017 The Author(s)

Learning algorithm for neuro-fuzzy Kolmogorov’s network based on the error back-propagation and 2-nd order optimization methods

В роботі розглянута нейро-фаззі мережа Колмогорова, що являє собою гібрид схеми суперпозиції функцій однієї змінної, нейронну мережу прямого розповсюдження та систему нечіткого виводу типу Такагі-Сугено. Запропонований новий пакетний градієнтний алгоритм навчання на основі зворотного поширення похибок та методів оптимізації другого порядку. Розглянута мережа та запропонований алгоритм навчання можуть бути використані для вирішення задач класифікації даних, прогнозування часових послідовностей, нейрокерування, емуляції та ін.The architecture of the neuro-fuzzy Kolmogorov’s network which is the hybrid of the superposition scheme of univariate functions, two-layer feed-forward neural network, and Takagi-Sugeno type fuzzy inference system is considered. A batch gradient-based learning procedure based on the error back-propagation and 2-nd order optimization methods is proposed. The considered network and the proposed learning algorithm can be applied to the problems of data classification, time-series prediction, neuro-control, emulation, etc.В работе рассмотрена нейро-фаззи сеть Колмогорова, которая представляет собой гибрид схемы суперпозиции функций одной переменной, нейронную сеть прямого распространения и систему нечеткого вывода типа Такаги-Сугено. Предложено новый пакетный градиентный алгоритм обучения на основе обратного распространения ошибок и методов оптимизации второго порядка. Рассмотренная сеть и предложенный алгоритм обучения могут быть использованы для решения задач классификации данных, прогнозирования временных последовательностей нейроуправления, эмуляции и др

Neuro-fuzzy Kolmogorov’s network with polynomial activation functions and its gradient-based learning algorithm

В роботi розглянута архiтектура нейро-фаззi-мережi Колмогорова, що являє собою гiбрид схеми суперпозицiї функцiй однiєї змiнної, двошарової нейронної мережi та дворiвневої системи нечiткого виведення. Ця мережа базується на використаннi дзвонуватих несиметричних полiномiальних функцiй приналежностi четвертого порядку. Запропонована градiєнтна процедура навчання для настроювання вагових коефiцiєнтiв та активацiйних функцiй вихiдного шару. Запропонована мережа може бути використана для вирiшення задач класифiкацiї даних, прогнозування часових послiдовностей, нейрокерування, емуляцiї та iн.The architecture of the neuro-fuzzy Kolmogorov’s network which is the hybrid of the superposition scheme of univariate functions, two-layer neural network, and multiresolution approach to a fuzzy reasoning is considered. Nonsymmetrical bell-shaped polynomial activation functions of the fourth order are used. A gradient-based learning procedure which tunes both the synaptic weights and activation functions of the output layer is proposed. The proposed network can be applied to the problems of data classification, time-series prediction, neuro-control, emulation, etc.В работе рассмотрена архитектура нейро-фаззи-сети Колмагорова, которыя представляет собой гибрид схемы суперпозиции функций одной переменной, двуслойной нейронной сети и двухуровневой системы нечеткого вывода. Эта сеть базируется на использовании колокольчатых несимметричных полиномиальных функций принадлежности четвертого порядка. Предложена градиентная процедура обучения для настройки весовых коэффициентов и активационных функций выходного слоя. Предложенная сеть может быть использована для решения задач классификации данных, прогнозирования временных последовательностей, нейроуправления, эмуляции и др

On adaptive fuzzy clustering algorithm

В статi розглянута задача кластеризацiї на основi вiрогiностного та можливостного пiдходiв за умови вiдсутностi навчаючого сигналу. Запропонована рекурентна модифiкацiя вiрогiностного та можливостного алгоритмiв кластеризацiї, якi виконуються паралельно. Комп’ютерне моделювання демонструє використання розробленого алгоритму для задач класифiкацiї даних та пiдтверджує його ефективнiсть.The problem of fuzzy clustering without training signal on the basis of the probabilistic and possibilistic approaches is considered. A recursive modification of the unsupervised probabilistic and possibilistic fuzzy clustering algorithms is proposed, which combines them in the parallel execution manner. Computer modeling confirms the effectiveness of the developed algorithm for solving data classification problems in uncertainty conditions