RF-LNA circuit synthesis using an array of artificial neural networks with constrained inputs

Abstract

Abstract-We describe a method for circuit synthesis that determines the parameter values by using a set of artificial neural networks (ANNs) that learn in sequence. Each ANN is optimized to output only one design parameter, and the latter constrains the learning/recall of its successor(s). Two competing ANN architectures are considered, the multilayer perceptron (MLP) and the radial basis functions (RBF) network, and each one has its internal parameters tuned by a genetic algorithm. The method was tested on the design of a radio-frequency, low-noise amplifier (RF-LNA) with ten design parameters to set, and it yielded one-hundred percent success rate in specifying the parameter values at five percent tolerance.Keywords-Synthesis, artificial neural network, genetic algorithm, multilayer perceptron, radial basis function, radiofrequency low noise amplifier.

Artificial Neural Network Modeling of a CMOS Differential Low-Noise Amplifier Using the Bayesian Regularization Algorithm

“…In [41], the modeling of CMOS LNA was achieved using the Adaptive Neuro-Fuzzy Inference System (ANFIS), demonstrating significantly lower errors than models developed using MLP and RBF. A different approach was described in [42], which outlined a method for RF LNA circuit synthesis determining parameter values through a set of ANNs aided by a Genetic Algorithm (GA). It is worth noting that GA required a relatively high number of generations.…”