Innovative Topologies and Algorithms for Neural Networks

Dear Colleagues,

The introduction of deep neural networks and new training procedures and topologies has solicited a renewed interest in the field of neural computation. Consequently, the use of deep structures has significantly improved the state-of-the-art in many application fields, such as computer vision, speech, language processing, soft sensors, and IoT, just to mention a few.

The chances of success of a neural network are largely linked to the adequate choice of the network architecture and the training algorithm. As a matter of fact, much of the recent interest on neural networks has been devoted to propose and study novel architectures, including solutions tailored to specific problems. Nevertheless, the choice of the structure, the network sizing, and hyperparameters selection, still are the subject of a vivid research interest.

The aim of this Special Issue is to bring together developments in the field of innovative topologies and algorithms for neural networks, along with applications relevant, but not limited, to industry and the IoT. Papers addressing the wide range of aspects of this technology are sought, including, but not limited to:

• Deep Belief Networks
• Autoencoders
• Long- Short-Term Memory networks
• Convolution Neural Networks
• Hierarchical-Deep models
• Other innovative structures
• Supervised learning
• Semi-supervised learning
• Unsupervised learning
• Ensemble learning
• Network structure selection and validation
• Neural networks for Data Fusion
• Computational intelligence methods for industrial applications
• Soft Sensors
• Neural Networks for the IoT and IIoT

Prof. Maria Gabriella Xibilia
Prof. Salvatore Graziani
Guest Editors

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