NDLI: New genetic algorithm approach for dynamic biochemical sensor measurements characterization

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New genetic algorithm approach for dynamic biochemical sensor measurements characterization

Content Provider	IEEE Xplore Digital Library
Author	Gantla, D. Abdel-Aty-Zohdy, Hoda S. Ewing, R.L.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Syst. Eng., Oakland Univ., Rochester, MI, USA (Gantla, D.; Abdel-Aty-Zohdy, H.S.)
Abstract	In an olfaction system (E-Nose) hardware implementation, outputs from the GA approach are used as inputs to an intelligent neural network system for biochemical detection and decision-making. In this paper we present a Genetic Algorithm for measurement characterization with dynamic inputs. Input measurements are from a given range and are assumed in parallel from chemical-sensor array. An input multiplexer/controller/Analog-Digital converter preprocessing stage is used to control these input measurements. The new dynamical approach presents measurement characterization and also optimum fused measurements without loosing the integrity of incoming signals. Through a novel mutation and crossover approach (Half Sibling and A Clone) optimum characteristic weight chromosomes are achieved. HSAC represents both crossover and mutation. A key feature of the new approach is that no pre-assigned minimum error is specified, rather error is dynamically evaluated based on measurements. Simulation results of the new GA with dynamic measurements are compared with one of the approaches from GAlib (A library of genetic Algorithm approaches from MIT) and proved the new approach has minimum error and a early convergence. MATLAB has been used as the simulation tool.
Sponsorship	IEEE Circuits & Syst. Soc. School of Electr. & Comput. Eng. at Oklahoma State Univ
File Size	282718
File Format	PDF
ISBN	0780375238
DOI	10.1109/MWSCAS.2002.1187151
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Biosensors Genetic mutations Neural network hardware Intelligent systems Intelligent networks Intelligent sensors Neural networks Decision making Chemicals
Content Type	Text
Resource Type	Article

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