Non-uniformity correction is a critical task for achieving higher performances in modern infrared imaging systems. Lately, special interest has been given to a scene-based adaptive non-uniformity correction approach based on a neural network with a steepest descent learning rule. However, low motion and some scene artifacts such as edges usually cause the production of ghosting-like artifacts o...

Adaptive online optimization algorithms, such as Adam, RMSprop, and AdaBound, have recently been tremendously popular they widely applied to address the issues in field of deep learning. Despite their prevalence prosperity, however, it is rare investigate distributed versions these adaptive algorithms. To fill gap, a subgradient learning algorithm over time-varying networks, called DAdaxBound, ...

Adaptive behavior in a changing world requires flexibly adapting one's rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the 'learning rate'). However, the brain mechanisms, and in particular the neuromodulators, involved in this pr...

Effective error-driven learning benefits from scaling of prediction errors to reward variability. Such behavioral adaptation may be facilitated by neurons coding prediction errors relative to the standard deviation (SD) of reward distributions. To investigate this hypothesis, we required participants to predict the magnitude of upcoming reward drawn from distributions with different SDs. After ...

the subject under investigation has been electronic learning (e-learning) or electronic education (e-education), specifically concentrating on virtual university. virtual university has been the subject of much debate in recent years. in short, the concept of virtual university denotes a learning/educational system/environment based on internet where higher education is promoted using tools and...

In this study, a new adaptive controller is proposed for position control of pneumatic systems. Difficulties associated with the mathematical model of the system in addition to the instability caused by Pulse Width Modulation (PWM) in the learning-based controllers using gradient descent, motivate the development of a new approach for PWM pneumatics. In this study, two modified Feedback Error L...

A new theorem for the development and convergence analysis of supervised training algorithms with an adaptive learning rate for each weight is presented. Based on this theoretical result, a strategy is proposed to automatically adapt the search direction, as well as the stepsize length along the resultant search direction. This strategy is applied to some well known local learning algorithms to...