Application of recursive least squares algorithm for learning and mathematical reasoning
| Autor: | A.R. Mirzai, C.F.N. Cowan, T.M. Crawford |
|---|---|
| Rok vydání: | 1990 |
| Předmět: |
Recursive least squares filter
business.industry Computer science Feature extraction Construct (python library) Mathematical reasoning Machine learning computer.software_genre Set (abstract data type) Artificial Intelligence Control and Systems Engineering Salient Pattern recognition (psychology) Artificial intelligence Electrical and Electronic Engineering business Algorithm computer |
| Zdroj: | Engineering Applications of Artificial Intelligence. 3:118-126 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/0952-1976(90)90005-7 |
| Popis: | In our recent publications, we have illustrated how pattern recognition and adaptive signal-processing techniques can be used to construct a machine learning system (MLS). The MLS is based on the manipulation of raw data to extract a set of salient features which have strong significance in the behaviour of the system or device under observation. This paper illustrates the application of the recursive least squares (RLS) algorithm, which has been used as a learning strategy in the MLS, for feature extraction and mathematical reasoning. Here a data-analysis program is developed, which can be used to explain the relationships between the inputs and the outputs of a system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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