Modeling & Simulation of 8bit Logarithmic Analog to Digital Converter using Applied Artificial Intelligence and Neural Network

Abstract

An artificial neural network (ANN), commonly referred to as a "neural network" (NN), is a computational model designed to mimic the structure and functionality of biological neural networks. It comprises interconnected artificial neurons and employs a connectionist approach to processing information. Typically, an ANN is adaptive, adjusting its structure based on internal or external information encountered during the learning phase. Modern neural networks serve as non-linear statistical data modeling tools, often utilized for capturing complex relationships between inputs and outputs or identifying patterns within datasets.

The Logarithmic Analog to Digital Converter (ADC) transforms an analog input signal into a logarithmic digital output. Primarily, it expands the dynamic range of the input, providing non-uniform quantization and compressing the signal into a digital format. This technology finds widespread application in fields such as communications, instrumentation, and medical devices like Deep Brain Stimulation. In this study, we aim to implement a Logarithmic ADC integrated using AI with a neural network on MATLAB, employing a perceptron design approach.