First Advisor

George G. Lendaris

Term of Graduation

Summer 1993

Date of Publication


Document Type


Degree Name

Master of Science (M.S.) in Electrical Engineering


Electrical Engineering




Continuous-time filters, Neural networks (Computer science), Signal processing -- Digital techniques



Physical Description

1 online resource (2, viii, 135 pages)


Component values of integrated filters vary considerably due to· manufacturing tolerances and environmental changes. Thus it is of major importance that the components of an integrated filter be electronically tunable. The method explored in this thesis is the transconductance-C-method.

A method of realizing higher-order filters is to use a cascade structure of second-order filters. In this context, a method of tuning second-order filters becomes important.

The research objective of this thesis is to determine if the Neural Network methodology can be used to facilitate the filter tuning process for a second-order filter (realized via the transconductance-C-method). Since this thesis is, at least to the knowledge of the author, the first effort in this direction, basic principles of filters and of Neural Networks [1-22] are presented.

A control structure is proposed which comprises three parts: the filter, the Neural Network, and a digital spectrum analyzer. The digital spectrum analyzer sends a test signal to the filter and measures the magnitude of the output at 49 frequency samples. The Neural Network part includes a memory that stores the 49 sampled values of the nominal spectrum. ·A comparator subtracts the latter values from the measured (actual) values, and feeds them as input to the Neural Network. The outputs of the Neural Network are the values of the percentage tuning amount The adjusting device, which is envisioned as a component of the filter itself, translates the output of the Neural Network to adjustments in the value of the filter's transconductances.

Experimental results provide a demonstration that the Neural Network methodology can be usefully applied to the above problem context. A feedforward, single-hidden-layer Backpropagation Network reduces the manufacturing errors of up to 85% for the pole frequency and of up to 41% for the quality factor down to less than approximately 5% each. It is demonstrated that the method can be iterated to further reduce the error.


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