{'fraction_non_alphanumeric': 0.020910209102091022, 'fraction_numerical': 0.0, 'mean_word_length': 5.359375, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
On the applicability of the NetFAN-approach to function approximation

Fuzzy systems were shown to be universal approximators, so are their trainable variant the neuro-fuzzy systems. But fuzzy systems suffer from the curse of dimensionality, i.e, a very strong increase in computational and memory demands with an increasing number of input variables. This paper describes a neuro-fuzzy method, the network of fuzzy adaptive nodes (NetFAN) approach, to reduce this drawback by decomposition. It also proofs that such decomposed systems are universal approximators. The benchmark example of modeling the energy and water consumption of a building not only demonstrates that it achieves approximation capabilities like artificial neural networks. It also gives a notion how to utilize abstract background knowledge.