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Input vectors for filters can be\nconstructed manually or with the assistance of included functions.\nSo far it is possible to use following filters:\n\n- LMS (least-mean-squares) adaptive filter\n\n- NLMS (normalized least-mean-squares) adaptive filter\n\n- LMF (least-mean-fourth) adaptive filter\n\n- NLMF (normalized least-mean-fourth) adaptive filter\n\n- SSLMS (sign-sign least-mean-squares) adaptive filter\n\n- NSSLMS (normalized sign-sign least-mean-squares) adaptive filter\n\n- RLS (recursive-least-squares) adaptive filter\n\n- GNGD (generalized normalized gradient descent) adaptive filter\n\n- AP (affine projection) adaptive filter\n\n- GMCC (generalized maximum correntropy criterion) adaptive filter\n\n- OCNLMS (online centered normalized least-mean-squares) adaptive filter\n\n- Llncosh (least lncosh) adaptive filter\n\n- Variable step-size least-mean-square (VSLMS) with Ang’s adaptation.\n\n- Variable step-size least-mean-square (VSLMS) with Benveniste’s adaptation\n\n- Variable step-size least-mean-square (VSLMS) with Mathews’s adaptation\n\n\n******************\nDetection Tools\n******************\n\nThe library features two novelty/outlier detection tools\n\n- Error and Learning Based Novelty Detection (ELBND)\n\n- Learning Entropy (LE)\n\n- Extreme Seeking Entropy (ESE)\n\n*************\nCite Padasip\n*************\n\n.. code-block:: none\n\n    @article{cejnek2022padasip,\n        title={Padasip: An open-source Python toolbox for adaptive filtering},\n        author={Cejnek, Matous and Vrba, Jan},\n        journal={Journal of Computational Science},\n        volume={65},\n        pages={101887},\n        year={2022},\n        publisher={Elsevier}\n    }\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatousc89%2Fpadasip","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatousc89%2Fpadasip","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatousc89%2Fpadasip/lists"}