This study focused on the identification problems of two-input single-output system with moving average noises based on unsupervised learning methods applied to the input signals. The input signal to the autoregressive moving average model is proposed to be arriving from a source with continuous technical and environmental changes as two separate featured input signals. These two input signals were grouped in a number of clusters using the K-means clustering algorithm. The clustered input signals were supplied to the model in an orderly fashion from cluster-1 up to cluster-K. To ensure that the output signal can be best predicted from the input signal which in turn leads to selecting good enough model for its intended use, the magnitude-squared coherence (MSC) measure is applied to the input/output signals in the cases of clustered and nonclustered inputs, which indicates best correlation coefficient when measured with clustered inputs. From collected input-output signals, we deduce a K-means clustering based recursive least squares method for estimating the parameter of autoregressive moving average system. The simulation results indicate that the suggested method is effective.
The aim of this article is to investigate how to estimate parameters and states jointly for the linear stochastic system with deterministic control inputs. The cross-correlation between process noise and measurement noise in Kalman filtering re-formation cycles is utilized to derive a Kalman filtering with correlated noises based recursive generalized extended least squares (KF-CN-RGELS) algorithm for jointly estimating parameters and system states. The performance analysis of different correlation coefficients between process and measurement noises shows that the accuracy of the identified parameters and states is proportional to the positive correlation coefficients. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms.
This paper focuses on the numerical weight calculations for a given learning outcome related to electrical engineering program courses at the University of Science and Technology. The paper proposes a formulation of different effective student learning outcomes selected from three different domains such as knowledge, skills, and values. The student learning outcomes were reformulated and distributed carefully to different methods of course evaluation based on specified teaching strategies. The contribution of each outcome through the whole course is calculated and distributed cumulatively to each teaching strategy used. This calculated weight is used to support the direct assessment method of course evaluation criteria by indicating the percentage of actual student achievement in each specified learning outcome concerning target and benchmark values. The effectiveness of the proposed method is verified by numerical examples.
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