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Updated: Jun 1, 2026

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
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Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

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Improved probabilistic neural network algorithm for chemical sensor array pattern recognition.

R E Shaffer1, S L Rose-Pehrsson

  • 1Chemistry Division, Naval Research Laboratory, Code 6116, 4555 Overlook Avenue, SW, Washington, D.C. 20375.

Analytical Chemistry
|June 14, 2011
PubMed
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This summary is machine-generated.

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An improved probabilistic neural network (IPNN) enhances chemical sensor array pattern recognition. This algorithm reduces computational needs, speeds training, and lowers false alarms for surface acoustic wave sensors.

Area of Science:

  • Artificial Intelligence
  • Chemical Sensing
  • Signal Processing

Background:

  • Probabilistic neural networks (PNNs) are effective for pattern recognition in chemical sensor arrays.
  • Conventional PNNs can be computationally intensive and require significant memory.
  • Optimizing PNN parameters like kernel width and rejection threshold is crucial for performance.

Purpose of the Study:

  • To introduce an improved probabilistic neural network (IPNN) algorithm for chemical sensor array pattern recognition.
  • To enhance the efficiency and accuracy of PNNs by reducing computational and memory demands.
  • To decrease the false alarm rate in sensor array data analysis.

Main Methods:

  • Developed an IPNN based on a modified PNN incorporating three key innovations.

Related Experiment Videos

Last Updated: Jun 1, 2026

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

  • Implemented a competitive learning strategy from learning vector quantization (LVQ) neural networks to reduce storage and computation.
  • Utilized a distance-based calculation for approximating optimal PNN kernel width, reducing training time and user input.
  • Applied Monte Carlo simulations to establish an optimal rejection threshold for outlier rejection.

    • Main Results:

    • The IPNN's hidden layer requires significantly less storage space compared to conventional PNNs.
    • The distance-based kernel width approximation decreased training time without user intervention.
    • The Monte Carlo-derived rejection threshold effectively identified and rejected ambiguous patterns.
    • Demonstrated IPNN utility on simulated and laboratory surface acoustic wave sensor array data sets.

    Conclusions:

    • The IPNN algorithm offers a more efficient and effective solution for chemical sensor array pattern recognition.
    • Innovations in the IPNN lead to reduced computational load, faster training, and improved accuracy.
    • The IPNN successfully minimizes false alarms through optimized parameter selection and outlier rejection.