A Current-Mode Analog Circuit for Reinforcement Learning Problems

Background: The original filter including grounded or virtual ground capacitors can be synthesized by using the NAM expansion. However, so far the filters including floating capacitor, such as Sallen-Key filter, have not been synthesized by means of the NAM expansion. This is a problem to be researched further. Methods: By using the adjoint network theory, the Sallen-Key filter including floating capacitor first is turned into a current-mode one, which includes a grounded capacitor and a virtual ground capacitor. Then the node admittance matrix, after derived, is extended by using NAM expansion. Results: At last, one VDTA Sallen-Key filter is received. It employs single compact VDTA and two grounded capacitors. Conclusion: A Butterworth VDTA second-order frequency filter based on Sallen-Key topology with fo = 100kHz, HLP = -HBP=1, is designed.

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CDBA based current instrumentation amplifier

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v4i0.62 ◽

2016 ◽

Vol 4 ◽

pp. 11 ◽

Cited By ~ 3

Author(s):

Priyanka Gupta ◽

Kunal Gupta ◽

Neeta Pandey ◽

Rajeshwari Pandey

Keyword(s):

Current Mode ◽

Instrumentation Amplifier ◽

Pspice Simulations ◽

Differential Gain ◽

Novel Method ◽

Current Difference ◽

A Current

This paper presents a novel method to realize a current mode instrumentation amplifier (CMIA) through CDBA (Current difference Buffered Amplifier). It employs two CDBAs and two resistors to obtain desired functionality. Further, it does not require any resistor matching. The gain can be set according to the resistor values. It offers high differential gain and a bandwidth, which is independent of gain. The working of the circuit is verified through PSPICE simulations using CFOA IC based CDBA realization.

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Learning and control

10.1093/oso/9780199674923.003.0026 ◽

2018 ◽

Author(s):

Ivan Herreros

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Brain Function ◽

Control Strategies ◽

Learning Problems ◽

Animal Learning ◽

Feed Forward Control ◽

Machine Learning Applications ◽

And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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