102 Rule Model Order Reduction of Integrated Circuit Based on Truncation Balanced Reduction Algorithm

Static condensation is widely used as a model order reduction technique to reduce the computational effort and complexity of classical continuum-based computational models, such as finite-element models. Peridynamic theory is a nonlocal theory developed primarily to overcome the shortcoming of classical continuum-based models in handling discontinuous system responses. In this study, a model order reduction algorithm is developed based on the static condensation technique to reduce the order of peridynamic models. Numerical examples are considered to demonstrate the robustness of the proposed reduction algorithm in reproducing the static and dynamic response and the eigenresponse of the full peridynamic models.

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A correlation-based model order reduction approach for a diesel engine NOx and brake mean effective pressure dynamic model using machine learning

International Journal of Engine Research ◽

10.1177/1468087420936949 ◽

2020 ◽

pp. 146808742093694

Author(s):

Armin Norouzi ◽

Masoud Aliramezani ◽

Charles Robert Koch

Keyword(s):

Support Vector Machine ◽

Model Order Reduction ◽

Order Reduction ◽

High Order ◽

Support Vector ◽

Effective Pressure ◽

Reduction Algorithm ◽

Order Model ◽

Model Order ◽

Low Order

A correlation-based model order reduction algorithm is developed using support vector machine to model [Formula: see text] emission and break mean effective pressure of a medium-duty diesel engine. The support vector machine–based model order reduction algorithm is used to reduce the number of features of a 34-feature full-order model by evaluating the regression performance of the support vector machine–based model. Then, the support vector machine–based model order reduction algorithm is used to reduce the number of features of the full-order model. Two models for [Formula: see text] emission and break mean effective pressure are developed via model order reduction, one complex model with high accuracy, called high-order model, and the other with an acceptable accuracy and a simple structure, called low-order model. The high-order model has 29 features for [Formula: see text] and 20 features for break mean effective pressure, while the low-order model has nine features for [Formula: see text] and six features for break mean effective pressure. Then, the steady-state low-order model and high-order model are implemented in a nonlinear control-oriented model. To verify the accuracy of nonlinear control-oriented model, a fast response electrochemical [Formula: see text] sensor is used to experimentally study the engine transient [Formula: see text] emissions. The high-order model and low-order model support vector machine models of [Formula: see text] and break mean effective pressure are compared to a conventional artificial neural network with one hidden layer. The results illustrate that the developed support vector machine model has shorter training times (5–14 times faster) and higher accuracy especially for test data compared to the artificial neural network model. A control-oriented model is then developed to predict the dynamic behavior of the system. Finally, the performance of the low-order model and high-order model is evaluated for different rising and falling input transients at four different engine speeds. The transient test results validate the high accuracy of the high-order model and the acceptable accuracy of low-order model for both [Formula: see text] and break mean effective pressure. The high-order model is proposed as an accurate virtual plant while the low-order model is suitable for model-based controller design.

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Passivity Preserving Model Order Reduction Using the Reduce Norm Method

Electronics ◽

10.3390/electronics9060964 ◽

2020 ◽

Vol 9 (6) ◽

pp. 964

Author(s):

Namra Akram ◽

Mehboob Alam ◽

Rashida Hussain ◽

Asghar Ali ◽

Shah Muhammad ◽

...

Keyword(s):

Integrated Circuits ◽

Integrated Circuit ◽

Model Order Reduction ◽

High Speed ◽

Order Reduction ◽

Reliable Operation ◽

Model Order ◽

Reduced Order ◽

Reduction Methods ◽

On Chip

Modeling and design of on-chip interconnect, the interconnection between the components is becoming the fundamental roadblock in achieving high-speed integrated circuits. The scaling of interconnect in nanometer regime had shifted the paradime from device-dominated to interconnect-dominated design methodology. Driven by the expanding complexity of on-chip interconnects, a passivity preserving model order reduction (MOR) is essential for designing and estimating the performance for reliable operation of the integrated circuit. In this work, we developed a new frequency selective reduce norm spectral zero (RNSZ) projection method, which dynamically selects interpolation points using spectral zeros of the system. The proposed reduce-norm scheme can guarantee stability and passivity, while creating the reduced models, which are fairly accurate across selected narrow range of frequencies. The reduced order results indicate preservation of passivity and greater accuracy than the other model order reduction methods.

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