Minimum input selection of reconfigurable architecture systems for structural controllability

2018 ◽  
Vol 62 (1) ◽  
Author(s):  
Ting Bai ◽  
Shaoyuan Li ◽  
Yuanyuan Zou
Keyword(s):  
Reconfigurable Architecture ◽  
Input Selection ◽  
Structural Controllability ◽  
Selection Of

Actuating Input Selection of 5-UPS/PRPU Parallel Machine Tool

2005 ◽  
Author(s):  
Jian-She Gao ◽  
Ren-Cheng Zheng ◽  
Yong-Sheng Zhao
Keyword(s):  
Parallel Mechanism ◽  
Basic Problem ◽  
Screw Theory ◽  
Parallel Machine ◽  
Condition Numbers ◽  
Constraint Matrix ◽  
Input Selection ◽  
Moving Platform ◽  
Parallel Machine Tool ◽  
Selection Of

The actuating input selection is an important basic problem for the parallel mechanism. Based on the screw theory, the constraint screw can be got after locking a kinematic pair in any limb, which can be taken as actuating wrench acted on the moving platform of the parallel mechanism. The constraint screw matrix is composed of the structure constraint screws and the constraint screws of the actuating pairs. The reasonableness of input selection can be judged by the rank of the constraint matrix. The performance of the combinations of actuating inputs is evaluated by the condition numbers of the force constraint matrix and the torque constraint matrix respectively. The theory presented is validated by the simulation and the maching test.

scholarly journals PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019)

2019 ◽  
Vol 8 (4) ◽  
pp. 451-461
Author(s):  
Khusnul Umi Fatimah ◽  
Tarno Tarno ◽  
Abdul Hoyyi
Keyword(s):  
Lagrange Multiplier ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lagrange Multiplier Test ◽  
Inference System ◽  
Input Selection ◽  
Lm Test ◽  
Rice Prices ◽  
Selection Of

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and  with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

scholarly journals Input Selection Methods for Soft Sensor Design: A Survey

2020 ◽  
Vol 12 (6) ◽  
pp. 97
Author(s):  
Francesco Curreri ◽  
Giacomo Fiumara ◽  
Maria Gabriella Xibilia
Keyword(s):  
Selection Procedure ◽  
Time Estimation ◽  
Critical Issue ◽  
Industrial Processes ◽  
Soft Sensors ◽  
Input Selection ◽  
Historical Databases ◽  
Real Time Estimation ◽  
Input Variables ◽  
Selection Of

Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this work and classified with their benefits and drawbacks to guide the designer through this step.

Optimal selection of essential interconnections for structural controllability in heterogeneous subsystems

Automatica ◽  
2019 ◽  
Vol 103 ◽  
pp. 424-434 ◽  
Author(s):  
Shana Moothedath ◽  
Prasanna Chaporkar ◽  
Madhu N. Belur
Keyword(s):  
Optimal Selection ◽  
Structural Controllability ◽  
Selection Of

Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling

2018 ◽  
Vol 33 (3) ◽  
pp. 955-973 ◽  
Author(s):  
Muhammad Shoaib ◽  
Asaad Y. Shamseldin ◽  
Sher Khan ◽  
Muhammad Sultan ◽  
Fiaz Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Input Selection ◽  
Selection Of

IMPACT ASSESSMENT OF INPUT OMISSION ON DEA

2005 ◽  
Vol 04 (03) ◽  
pp. 359-368 ◽  
Author(s):  
JOHN RUGGIERO
Keyword(s):  
Data Envelopment Analysis ◽  
Simulation Analysis ◽  
Data Envelopment ◽  
Careful Selection ◽  
Reliable Measurement ◽  
Input Selection ◽  
Statistical Procedures ◽  
Relevant Variables ◽  
Selection Of

It is well known in the Data Envelopment Analysis literature that proper variable selection is necessary for the reliable measurement of efficiency. Omitting production relevant variables and/or including irrelevant variables will lead to biased measurement. It is also known that the sample size needs to be large relative to the number of inputs and outputs to prevent classification of efficiency by default. In some empirical settings the number of potential relevant variables is large. Careful selection of an appropriate set of variables is necessary for reliable efficiency measurement. This paper looks at the issue of input selection and uses simulation analysis to develop statistical procedures to provide guidelines for input selection.

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