Advances and New Trends in Modeling and Control of Neural Network Models
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".
Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 12160
Special Issue Editors
2. Department of Mathematics, University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: nonlinear analysis; control theory; mathematical modeling; differential equations; fractional calculus
Special Issues, Collections and Topics in MDPI journals
Interests: nonlinear analysis; mathematical modeling; fractional order systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Due to the impressive applications of neural network systems in significant fields in science and technology such as pattern recognition, associative memory, optimization, linear and nonlinear programing, and computer vision, the research on their fundamental and qualitative behavior has attracted the attention of a considerable audience of professionals. As a result, modeling, analysis, and control methods for neural network models have emerged as fundamental tools in pure and applied research. Additionally, the rapid development of large-scale computers and parallel computations has highly increased the industrial recognition of the use of neural network models for solving problems in technology as well as the number of strategies for their hardware implementation.
In this Special Issue, we provide an international forum for researchers to contribute original research focusing on the latest achievements and new trends in the modeling and control of neural network systems.
Prof. Dr. Gani Stamov
Dr. Ivanka Stamova
Guest Editors
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Keywords
- Hopfield neural networks
- Cellular neural networks
- Bidirectional associative memory neural networks
- Lotka–Volterra neural networks
- Neural networks with delays
- Impulsive neural networks
- Cohen–Grossberg neural networks
- Reaction–diffusion neural networks
- Fractional neural networks
- Stability
- Periodicity
- Almost periodicity
- Modeling
- Control
- Stabilization
- Applications in science and technology
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