EE 236: State and Parameter Estimation Theory

← 2015-16 2016-17 2017-18 →
← 2014-15 ← 2014-15 changes (lax) changes (strict) ▾ 2019-20 → 2017-18 →

Units: 4

Hours: Lecture, 3 hours; discussion, 1 hour

Catalog page 256

Prerequisites: EE 215

Description: Covers auto-regressive and moving-average models; state estimation and parameter identification (including least square and maximum likelihood formulations); observability theory; synthesis of optimum inputs; Kalman-prediction (filtering and smoothing); steady-state and frequency domain analysis; online estimation; colored noise; and nonlinear filtering algorithms.

Cross-listing: Cross-listed with ME 236.

Derived Information — The following is not part of the official catalog but is computed from catalog data.

Serves as a prerequisite for

EE 245 ME 222
Prerequisite graph not available.
Enrollment History (from UCR Banner, not catalog)
Combined: EE 236 / ME 236
Year F W S Su Total
2025-26  8/45  8/45
2024-25  9/45  9/45
2023-24 7/30  7/30
2022-23 15/45 15/45
2021-22 19/45 19/45
2020-21 22/45 22/45
2019-20 21/45 21/45
2018-19 32/39 32/39
2017-18 25/39 25/39
2016-17 17/31 17/31