Producción Científica Profesorado

A Neuro Fuzzy Solution in the Design of Analog Circuits

Seck Tuoh Mora, Juan Carlos


Miranda-Romagnoli, P., Hernández-Romero, N., & Seck-Tuoh Mora, J. C. (2011) A Neuro Fuzzy Solution in the Design of Anlog Circuits. IEICE TRANS. FUNDAMENTALS, E-94A (1), 434-439.


A neuro fuzzy method to design analog circuits is explained, where the universe of discourse of the fuzzy system is adjusted by means of a self-organized artificial neural network. As an example of this approach, an op-amp is optimized in order to hold a predetermined aim; where the unity gain bandwidth is an objective of design, and the restrictions of open-loop gain and margin phase are treated as objectives too. Firstly, the experience of the behavior of the circuit is obtained, hence an inference system is constructed and a neural network is applied to achieve a faster convergence into a desired solution. This approach is characterized by having a simple implementation, a very natural understanding and a better performance than static methods of fuzzy optimization.

Artículos relacionados

Elementary cellular automaton Rule 110 explained as a block substitution system

On explicit inversion of a subclass of operators with D-difference kernels and Weyl theory of the co...

Pair Diagram and Cyclic Properties Characterizing the Inverse of Reversible Automata

Complex Dynamics Emerging in Rule 30 with Majority Memory

How to Make Dull Cellular Automata Complex by Adding Memory: Rule 126 Case Study

Modeling a Nonlinear Liquid Level System by Cellular Neural Networks

Reproducing the Cyclic Tag System Developed by Matthew Cook with Rule 110 Using the Phases f(i-)1.

Unconventional invertible behaviors in reversible one-dimensional cellular automata.