Screen LibrarySystem identification using regular and quantized observations applications of large deviations principles /
This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new...
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| Group Author: | ; |
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| Literature type: | Electronic eBook |
| Language: | English |
| Series: |
SpringerBriefs in Mathematics
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| Online Access: |
http://dx.doi.org/10.1007/978-1-4614-6292-7 |
| Summary: |
This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications. |
| Carrier Form: | 1 online resource (xii, 95 p.) : ill. |
| ISBN: |
9781461462927 (electronic bk.) 1461462924 (electronic bk.) |
| Index Number: | QA402 |
| CLC: | N945.14 |
| Contents: |
Introduction and Overview -- System Identification: Formulation -- Large Deviations: An Introduction -- LDP of System Identification under Independentand Identically Distributed Observation Noises -- LDP of System Identification under Mixing Observation Noises -- Applications to Battery Diagnosis -- Applications to Medical Signal Processing -- Applications to Electric Machines -- Remarks and conclusion. |