Explicit form for input output function of linear time variable ladder networks

1993 ◽  
Vol 29 (18) ◽  
pp. 1615 ◽  
Author(s):  
J.J. Narraway
Keyword(s):  
Explicit Form ◽  
Linear Time ◽  
Time Variable ◽  
Output Function ◽  
Input Output

scholarly journals Identification of Brain Electrical Activity Related to Head Yaw Rotations

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3345
Author(s):  
Enrico Zero ◽  
Chiara Bersani ◽  
Roberto Sacile
Keyword(s):  
Electrical Activity ◽  
Electrical Stimulus ◽  
Head Movements ◽  
Brain Electrical Activity ◽  
Output Function ◽  
Input Output ◽  
Eeg Signals ◽  
Head Turning ◽  
Identification Technique ◽  
The Brain

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

Computer simulation of the steady-state input-output function of the cat medial gastrocnemius motoneuron pool

1991 ◽  
Vol 65 (4) ◽  
pp. 952-967 ◽  
Author(s):  
C. J. Heckman ◽  
M. D. Binder
Keyword(s):  
Experimental Data ◽  
Steady State ◽  
Motor Unit ◽  
Frequency Modulation ◽  
Motor Units ◽  
Medial Gastrocnemius ◽  
Output Function ◽  
Input Output ◽  
Motoneuron Pool ◽  
Random Variance

1. A pool of 100 simulated motor units was constructed in which the steady-state neural and mechanical properties of the units were very closely matched to the available experimental data for the cat medial gastrocnemius motoneuron pool and muscle. The resulting neural network generated quantitative predictions of whole system input-output functions based on the single unit data. The results of the simulations were compared with experimental data on normal motor system behavior in humans and animals. 2. We considered only steady-state, isometric conditions. All motoneurons received equal proportions of the synaptic input, and no feedback loops were operative. Thus the intrinsic properties of the motor unit population alone determined the form of the system input-output function. Expressing the synaptic input in terms of effective synaptic current allowed the simulated motoneuron input-output functions to be specified by well-known firing rate-injected current relations. The motor unit forces were determined from standard motor unit force-frequency relations, and the system output at any input level was assumed to be the linear sum of the forces of the active motor units. 3. The steady-state input-output function of the simulated motoneuron pool had a roughly sigmoidal shape that was quite different from those derived from previous recruitment models, which did not incorporate frequency modulation. Frequency modulation in combination with the skewed distribution of thresholds (low values much more frequent than high) restricted upward curvature to low input levels, whereas frequency modulation alone was responsible for the final gradual approach to the maximum force output. 4. Sensitivity analyses were performed to assess the importance of several assumptions that were required to deal with gaps and uncertainties in the available experimental data. The shape of the input-output function was not critically dependent on any of these assumptions, including those specifying linear summation of inputs and outputs. 5. A key assumption of the model was that systematic variance in motor unit properties was much more important than random variance for determining the input-output function. Addition of random variance via Monte Carlo techniques showed that this assumption was correct. These results suggest that the output of a motoneuron pool should be quite tolerant of random variance in the distribution of synaptic inputs and yet substantially altered by any systematic differences, such as unequal distribution of inputs among different motor unit types.(ABSTRACT TRUNCATED AT 400 WORDS)

Minimum order input-output equation for linear time-varying digital filters

1998 ◽  
Vol 5 (7) ◽  
pp. 171-173 ◽  
Author(s):  
Cishen Zhang ◽  
Song Wang ◽  
Yu Fan Zheng
Keyword(s):  
Digital Filters ◽  
Linear Time ◽  
Time Varying ◽  
Input Output ◽  
Minimum Order ◽  
Output Equation

scholarly journals Input–output linearization of non-linear time-varying delay systems: the single-input single-output case

2019 ◽  
Vol 37 (3) ◽  
pp. 831-854
Author(s):  
Ihab Haidar ◽  
Florentina Nicolau ◽  
Jean-Pierre Barbot ◽  
Woihida Aggoune
Keyword(s):  
Linear Time ◽  
Sufficient Conditions ◽  
Delay Systems ◽  
Time Varying ◽  
Input Output ◽  
Time Varying Delay ◽  
Output Stabilization ◽  
Non Linear ◽  
Varying Delay ◽  
Input Output Linearization

Abstract This paper deals with the input–output linearization of non-linear time-varying delay systems. We introduce an extension of the Lie derivative for time-varying delay systems and derive sufficient conditions for the existence of a causal and bounded non-linear feedback linearizing the input–output behaviour of the system. Sufficient conditions ensuring the internal stability after output stabilization are also presented. Finally, several examples illustrating our main results are discussed.

scholarly journals On the Input-Output Distinguishability of Single Output Continuous Linear Time-Invariant Systems

2016 ◽  
Vol 61 (7) ◽  
pp. 1906-1911 ◽  
Author(s):  
Koffi M. D. Motchon ◽  
Komi M. Pekpe ◽  
Jean-Philippe Cassar ◽  
Stephan De Bievre
Keyword(s):  
Linear Time ◽  
Continuous Linear ◽  
Input Output ◽  
Linear Time Invariant ◽  
Single Output ◽  
Linear Time Invariant Systems ◽  
Time Invariant
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