Neuro-symbolic integration using pseudo inverse rule

2010 ◽  
Author(s):  
Saratha Sathasivam ◽  
Muraly Velavan
Keyword(s):  
Symbolic Integration ◽  
Inverse Rule ◽  
Pseudo Inverse

Applying Different Learning Rules in Neuro-Symbolic Integration

2012 ◽  
Vol 433-440 ◽  
pp. 716-720
Author(s):  
Saratha Sathasivam
Keyword(s):  
Computer Simulation ◽  
Learning Rule ◽  
Computation Time ◽  
Activation Function ◽  
The Other ◽  
Symbolic Integration ◽  
Learning Rules ◽  
Hebb Rule ◽  
Pseudo Inverse ◽  
Better Than

Pseudo inverse learning rule and new activation unction performance will be evaluated and compared with the primitive learning rule, Hebb rule. Comparisons are made between these three rules to see which rule is better or outperformed other rules in the aspects of computation time, memory and complexity. From the computer simulation that has been carried out, the new activation function performs better than the other two learning methods.

Error analyses of a Multi-Input-Multi-Output cantilever beam test

2021 ◽  
pp. 107754632110337
Author(s):  
Arup Maji ◽  
Fernando Moreu ◽  
James Woodall ◽  
Maimuna Hossain
Keyword(s):  
Transfer Function ◽  
Frequency Domain ◽  
Cantilever Beam ◽  
Transfer Functions ◽  
Linear Superposition ◽  
Transfer Function Matrix ◽  
Error Analyses ◽  
Pseudo Inverse ◽  
Function Matrix

Multi-Input-Multi-Output vibration testing typically requires the determination of inputs to achieve desired response at multiple locations. First, the responses due to each input are quantified in terms of complex transfer functions in the frequency domain. In this study, two Inputs and five Responses were used leading to a 5 × 2 transfer function matrix. Inputs corresponding to the desired Responses are then computed by inversion of the rectangular matrix using Pseudo-Inverse techniques that involve least-squared solutions. It is important to understand and quantify the various sources of errors in this process toward improved implementation of Multi-Input-Multi-Output testing. In this article, tests on a cantilever beam with two actuators (input controlled smart shakers) were used as Inputs while acceleration Responses were measured at five locations including the two input locations. Variation among tests was quantified including its impact on transfer functions across the relevant frequency domain. Accuracy of linear superposition of the influence of two actuators was quantified to investigate the influence of relative phase information. Finally, the accuracy of the Multi-Input-Multi-Output inversion process was investigated while varying the number of Responses from 2 (square transfer function matrix) to 5 (full-rectangular transfer function matrix). Results were examined in the context of the resonances and anti-resonances of the system as well as the ability of the actuators to provide actuation energy across the domain. Improved understanding of the sources of uncertainty from this study can be used for more complex Multi-Input-Multi-Output experiments.

Pseudo-Inverse MMSE Based QRD-M Algorithm for MIMO OFDM

2006 ◽  
Author(s):  
Sumei Sun ◽  
Yongmei Dai ◽  
Zhongding Lei ◽  
H. Kenichi ◽  
H. Kawai
Keyword(s):  
Mimo Ofdm ◽  
Pseudo Inverse

Separation of temperature and strain in a single fiber BOTDA system by pseudo-inverse approach

2017 ◽  
Author(s):  
Felipe Lima dos Reis Marques ◽  
Claudio Floridia ◽  
Thayane Alves Almeida ◽  
Ariovaldo Antonio Leonardi ◽  
Fabiano Fruett
Keyword(s):  
Single Fiber ◽  
Inverse Approach ◽  
Pseudo Inverse
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