Dynamisches unüberwachtes Feed Forward Neural Netzwerk Clusterung

Artificial neural networks are computation models inspired by neurobiology, designed to enhance and test computational analogies of neurons. In a neural feedforward network (FFNN), data processing occurs in a single forward connection from the input layer to the output layer without any feedback loops. Unsupervised FFNN (UFFNN) clustering possesses remarkable capabilities such as inherent distributed parallel processing architectures, adjustment of connection weights for learning and categorizing data into meaningful groups with specific objectives, classification of related data into similar groups without the use of class labels, handling noisy data, and learning the types of input data values based on their weights and characteristics. Generally, dynamic data in real-world environments is very extensive and high-dimensional, thus dynamic online UFFNN clustering methods should be developed to enable incremental online learning capabilities.