On the entropy of continuous probability distributions (Corresp.)

Lazo, A.V.; Rathie, Pushpa N.

doi:10.1109/tit.1978.1055832

Cited by 155 publications

(73 citation statements)

References 5 publications

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“…Literature [44] listed several entropy of continuous probability distribution, and the entropy of continuous system with obeys normal distribution is:…”

Section: Algorithm Convergence Analysesmentioning

confidence: 99%

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

Ruan¹,

Chen²,

Dai³

2011

ICA

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A novel motor learning method is present based on the cooperation of the cerebellum and basal ganglia for the behavior learning of agent. The motor learning method derives from the principle of CNS and operant learning mechanism and it depends on the interactions between the basal ganglia and cerebellum. The whole learning system is composed of evaluation mechanism, action selection mechanism, tropism mechanism. The learning signals come from not only the Inferior Olive but also the Substantia Nigra in the beginning. The speed of learning is increased as well as the failure time is reduced with the cerebellum as a supervisor. Convergence can be guaranteed in the sense of entropy. With the proposed motor learning method, a motor learning system for the self-balancing two-wheeled robot has been built using the RBF neural networks as the actor and evaluation function approximator. The simulation experiments showed that the proposed motor learning system achieved a better learning effect, so the motor learning based on the coordination of cerebellum and basal ganglia is effective.

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“…Literature [44] listed several entropy of continuous probability distribution, and the entropy of continuous system with obeys normal distribution is:…”

Section: Algorithm Convergence Analysesmentioning

confidence: 99%

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

Ruan¹,

Chen²,

Dai³

2011

ICA

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“…Decreasing the resistance level may also result in lower fatigue probability and less frequent compensatory motion, which in turn may lead to longer duration of the exercise. To include these features into the current system, we are currently formulating a new probabilistic framework that models the users ability using Beta distributions [18] as a function of continuous resistance levels.…”

Section: Future Workmentioning

confidence: 99%

A decision-theoretic approach in the design of an adaptive upper-limb stroke rehabilitation robot

Huq

Kan

Goetschalckx

et al. 2011

2011 IEEE International Conference on Rehabilitation Robotics

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Abstract-This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a partially observable Markov decision process (POMDP) as its primary engine for decision-making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises. The performance of the system was evaluated through various simulations and by comparing the decisions made by the system with those of a human therapist for a single patient. In general, the simulations showed promising results and the therapist thought the system decisions were believable.

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“…In information theory, the entropy was introduced by Claude Shannon [3] in 1948 for random variables with a discrete probability space. A natural extension of a discrete entropy is a differential entropy [4] defined for a continuous random variable with probability density function (pdf) p(x) by:…”

Section: Introductionmentioning

confidence: 99%

Improvement of the k-nn Entropy Estimator with Applications in Systems Biology

Charzyńska¹,

Gambin

2015

Entropy

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Abstract:In this paper, we investigate efficient estimation of differential entropy for multivariate random variables. We propose bias correction for the nearest neighbor estimator, which yields more accurate results in higher dimensions. In order to demonstrate the accuracy of the improvement, we calculated the corrected estimator for several families of random variables. For multivariate distributions, we considered the case of independent marginals and the dependence structure between the marginal distributions described by Gaussian copula. The presented solution may be particularly useful for high dimensional data, like those analyzed in the systems biology field. To illustrate such an application, we exploit differential entropy to define the robustness of biochemical kinetic models.

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On the entropy of continuous probability distributions (Corresp.)

Cited by 155 publications

References 5 publications

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

A decision-theoretic approach in the design of an adaptive upper-limb stroke rehabilitation robot

Improvement of the k-nn Entropy Estimator with Applications in Systems Biology

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