{'fraction_non_alphanumeric': 0.021437578814627996, 'fraction_numerical': 0.0, 'mean_word_length': 6.0265486725663715, '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}}
Behavior stabilization of complex-valued recurrent neural networks using relative-minimization learning

Relative-minimization learning using additional random teacher signals is proposed for recurrent-behavior stabilization. Although the recurrent neural networks can deal with time-sequential data, they tend to show an unstable behavior (positive Lyapunov exponent). The proposed method superimposes a type of basin upon a dynamics-determining hypersurface in an information vector field. This process is equivalent to the relative minimization of the error function in the input-signal partial space. Experiments demonstrate that the relative-minimization learning suppresses positive values of Lyapunov exponents down to zero or negative, resulting in a successful behavior stabilization.