Dynamics in Neural Networks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 10580
Special Issue Editors
Interests: artificial intelligence; neural networks; engineering; informatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As a method of computational intelligence, artificial neural networks and other methods of computational intelligence have intertwined to develop a variety of new neural networks, such as convolutional neural networks, BP neural networks, and evolutionary neural networks, which have both practical value and developmental promise. Despite the remarkable development and application of neural networks, the uncertainty of the network itself makes it a time-consuming process to find a suitable network in the process of design and use; the design of artificial neural networks is an extremely complex dynamic optimization work for a specific problem, and there is still no systematic rule to follow. Although neural networks have a relatively sound theoretical foundation, computational intelligence techniques like evolutionary computation and other important dynamic optimization techniques do not yet have a sound mathematical foundation. The analysis and proof of the stability and convergence of computational intelligence algorithms are still in the research stage. Testing their effectiveness and efficiency through numerical experimental methods and specific applications is still the main method for the study of computational intelligence algorithms.
This Special Issue hopes to serve as a good international exchange platform for researchers in various fields to summarize the latest progress and ideas in neural computing and other fields related to neural networks.
Prof. Dr. Zheng Tang
Dr. Yuki Todo
Guest Editors
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Keywords
- artificial intelligence
- neural networks
- computational intelligence
- big data
- computational neural models
- brain-like systems
- optimization problems