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In this brief, we study the classical RLC resonance network (which is a two-state network) when one or more of its re-active elements is controlled by the state variable of the other. In particular, we consider the case when the capacitor is current-controlled by the inductor current and the case when the inductor is voltage-controlled by the capacitor voltage as well as the case of simultaneous mutual control. These cases are termed inter-state-controlled resonance networks due to the embedded self feedback and are found to produce chaotic signals. Circuit simulations of an analog realization and experimental results from a digital circuit are provided to verify the theory.

We propose a new class of nonlinear resonance networks which are modeled by standard second-order nonlinear differential equations of the Levinson-Smith type or its subset, the Liénard type. In particular, we modify series and parallel RLC resonance networks such that the passive resistor in these networks is composed of a fixed part and a variable voltage- or current-controlled part. The variable resistor is then made controllable by one of the circuit’s state-space variables leading to an embedded self feedback control mechanism. A possible discrete component circuit realizing the proposed concept is presented along with its simulations. The filter response behavior of one of the modified series resonance circuits is also experimentally verified using a Field Programmable Analog Array.

This paper systematically investigates the design of single transistor second-order active filters out-lining all possible architectures and possible impedance settings using an exhaustive MAPLE search code for all stable cases with the minimum number of passive elements (two resistors and two energy storage elements). The search is performed on six general voltage-input voltage-output transfer functions, derived using a small-signal two-port network transistor model. Selected LC and RC filter designs are verified experimentally. The results presented here fill both a circuit-theoretic gap and an application-oriented gap related to single transistor filter design which, apart from a few examples in the literature, has not been rigorously addressed. [ABSTRACT FROM AUTHOR]

Observed spatiotemporal firings of biological neurons have lead many researchers to believe that the rate of firings of these biological neurons is what conveys neuronal information in the brain. In this paper we seek to highlight parallels between biological neurons and observed effects in real neurons, with artificial neurons implemented as switched-capacitor structures. One such effect is the heavy use of lateral inhibition observed in the brain that is often modeled by winner-take-all analog circuits. This paper introduces a novel winner-take-all circuit using switched capacitors that truly mimics this effect seen in biological systems. In addition, we show how switched-capacitor structures can also cater to both binary and bipolar coding of input data vectors, as required by many artificial neural network paradigms today. Applications of switched-capacitors artificial neural networks to pattern recognition and character recognition problems using feedforward associative neural networks are also discussed, and two examples are provided. Simulations using both HSPICE and SWITCAP2 confirm all our expectations.