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[Artificial Neural Network and its application to analytical chemistry]
1Laboratory of Advanced Spectroscopy, Nanjing University of Science & Technology, 210014 Naijing.
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|April 13, 2005
Summary
Artificial Neural Networks (ANNs) offer powerful tools for analytical chemistry, excelling in nonlinear calibration and pattern recognition tasks. This review covers ANNs theory, algorithms, and performance, citing 95 references.
Area of Science:
- Analytical Chemistry
- Computational Chemistry
- Artificial Intelligence
Context:
- Review of Artificial Neural Networks (ANNs) in analytical chemistry.
- Focus on theoretical basis, algorithms, and performance.
- Exploration of ANNs for nonlinear calibration and pattern recognition.
Purpose:
- To provide a comprehensive overview of ANNs for analytical chemists.
- To highlight the capabilities and applications of ANNs in the field.
- To serve as a reference for researchers and practitioners.
Summary:
- Discusses the theoretical underpinnings and algorithmic structures of ANNs.
- Details the performance characteristics and practical implementation of ANNs.
- Reviews diverse applications, including nonlinear calibration and pattern recognition.
Impact:
- Enhances understanding of ANNs' role in modern analytical chemistry.
- Facilitates the adoption of advanced computational methods.
- Supports innovation in chemical analysis and data interpretation.